Imports

from datetime import datetime
import pandas as pd

from diive.core.io.files import load_parquet, save_parquet
from diive.pkgs.gapfilling.xgboost_ts import XGBoostTS

Load data

df = load_parquet(filepath="17.1_CH-CHA_meteo10_2005-2024.parquet")
df

Loaded .parquet file 17.1_CH-CHA_meteo10_2005-2024.parquet (0.048 seconds).
    --> Detected time resolution of <30 * Minutes> / 30min 

	LW_IN_T1_2_1	PA_GF1_0.9_1	FLAG_PA_GF1_0.9_1_ISFILLED	PPFD_IN_T1_2_2	FLAG_PPFD_IN_T1_2_2_ISFILLED	VPD_T1_2_1	...	SWC_GF1_0.75_1	TS_GF1_0.04_1	TS_GF1_0.15_1	TS_GF1_0.4_1	FLAG_PREC_RAIN_TOT_GF1_0.5_1_FLUXNET_ISFILLED	TIMESINCE_PREC_RAIN_TOT_GF1_0.5_1
TIMESTAMP_MIDDLE
2005-01-01 00:15:00	NaN	978.100000	1.0	0.0	0	0.099893	...	NaN	NaN	NaN	NaN	NaN	1
2005-01-01 00:45:00	NaN	977.933333	1.0	0.0	0	0.097606	...	NaN	NaN	NaN	NaN	NaN	2
2005-01-01 01:15:00	NaN	977.900000	1.0	0.0	0	0.091683	...	NaN	NaN	NaN	NaN	NaN	0
2005-01-01 01:45:00	NaN	977.833333	1.0	0.0	0	0.071157	...	NaN	NaN	NaN	NaN	NaN	1
2005-01-01 02:15:00	NaN	977.833333	1.0	0.0	0	0.058333	...	NaN	NaN	NaN	NaN	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-31 21:45:00	304.613900	983.370890	NaN	0.0	0	0.000011	...	45.120877	3.474346	4.437078	5.528727	NaN	380
2024-12-31 22:15:00	303.039890	983.052160	NaN	0.0	0	0.000011	...	45.144937	3.428224	4.440415	5.521962	NaN	381
2024-12-31 22:45:00	302.093633	982.851140	NaN	0.0	0	0.000011	...	45.152280	3.384733	4.443751	5.523991	NaN	382
2024-12-31 23:15:00	302.217307	982.896827	NaN	0.0	0	0.000010	...	45.095043	3.349179	4.439747	5.528050	NaN	383
2024-12-31 23:45:00	298.392973	982.856613	NaN	0.0	0	0.000010	...	45.278093	3.316919	4.442417	5.523991	NaN	384

350640 rows × 23 columns

Gap-filling TS

[print(c) for c in df if "TS_" in c];

TS_GF1_0.04_1
TS_GF1_0.15_1
TS_GF1_0.4_1

Fill TS_GF1_0.04_1

TARGET_COL = 'TS_GF1_0.04_1'
TARGET_GAPFILLED_COL = f'{TARGET_COL}_gfXG'
FLAG_GAPFILLED_COL = f'FLAG_{TARGET_GAPFILLED_COL}_ISFILLED'

# Dataframe for gap-filling
_df = pd.DataFrame()
_df[TARGET_COL] = df[TARGET_COL].copy()
_df['TA_T1_2_1'] = df['TA_T1_2_1'].copy()

# XGBoost
xgb = XGBoostTS(
    input_df=_df,
    target_col=TARGET_COL,
    features_lag=[-5, -1],
    features_lag_exclude_cols=None,
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)
xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()
xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()
results = xgb.gapfilling_df_

# Add results to main data
df = pd.concat([df, results[[TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]]], axis=1)

# Plot
plotdf = df[[TARGET_COL, TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]].copy()
plotdf.plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));
locs = (plotdf.index.year == 2011) & (plotdf.index.month == 8)
plotdf[locs].plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-09-09 10:15:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:4.77951	validation_1-rmse:4.77196
[1]	validation_0-rmse:3.50458	validation_1-rmse:3.49880
[2]	validation_0-rmse:2.64362	validation_1-rmse:2.64112
[3]	validation_0-rmse:2.07531	validation_1-rmse:2.07526
[4]	validation_0-rmse:1.70842	validation_1-rmse:1.71063
[5]	validation_0-rmse:1.47499	validation_1-rmse:1.47882
[6]	validation_0-rmse:1.33445	validation_1-rmse:1.33946
[7]	validation_0-rmse:1.22991	validation_1-rmse:1.23687
[8]	validation_0-rmse:1.16118	validation_1-rmse:1.16975
[9]	validation_0-rmse:1.11181	validation_1-rmse:1.12216
[10]	validation_0-rmse:1.07220	validation_1-rmse:1.08290
[11]	validation_0-rmse:1.04681	validation_1-rmse:1.05823
[12]	validation_0-rmse:1.01730	validation_1-rmse:1.02939
[13]	validation_0-rmse:0.99110	validation_1-rmse:1.00399
[14]	validation_0-rmse:0.96122	validation_1-rmse:0.97487
[15]	validation_0-rmse:0.95195	validation_1-rmse:0.96584
[16]	validation_0-rmse:0.93773	validation_1-rmse:0.95178
[17]	validation_0-rmse:0.92262	validation_1-rmse:0.93705
[18]	validation_0-rmse:0.91796	validation_1-rmse:0.93260
[19]	validation_0-rmse:0.90913	validation_1-rmse:0.92432
[20]	validation_0-rmse:0.89492	validation_1-rmse:0.91017
[21]	validation_0-rmse:0.88829	validation_1-rmse:0.90392
[22]	validation_0-rmse:0.87392	validation_1-rmse:0.88977
[23]	validation_0-rmse:0.86861	validation_1-rmse:0.88474
[24]	validation_0-rmse:0.86132	validation_1-rmse:0.87797
[25]	validation_0-rmse:0.85329	validation_1-rmse:0.87001
[26]	validation_0-rmse:0.84629	validation_1-rmse:0.86399
[27]	validation_0-rmse:0.84228	validation_1-rmse:0.86025
[28]	validation_0-rmse:0.82948	validation_1-rmse:0.84766
[29]	validation_0-rmse:0.82443	validation_1-rmse:0.84259
[30]	validation_0-rmse:0.81865	validation_1-rmse:0.83697
[31]	validation_0-rmse:0.81032	validation_1-rmse:0.82826
[32]	validation_0-rmse:0.80613	validation_1-rmse:0.82463
[33]	validation_0-rmse:0.79825	validation_1-rmse:0.81654
[34]	validation_0-rmse:0.79385	validation_1-rmse:0.81247
[35]	validation_0-rmse:0.78938	validation_1-rmse:0.80817
[36]	validation_0-rmse:0.78403	validation_1-rmse:0.80279
[37]	validation_0-rmse:0.77763	validation_1-rmse:0.79578
[38]	validation_0-rmse:0.77188	validation_1-rmse:0.79002
[39]	validation_0-rmse:0.76876	validation_1-rmse:0.78732
[40]	validation_0-rmse:0.76482	validation_1-rmse:0.78360
[41]	validation_0-rmse:0.76153	validation_1-rmse:0.78042
[42]	validation_0-rmse:0.75761	validation_1-rmse:0.77662
[43]	validation_0-rmse:0.75505	validation_1-rmse:0.77429
[44]	validation_0-rmse:0.74784	validation_1-rmse:0.76718
[45]	validation_0-rmse:0.74436	validation_1-rmse:0.76362
[46]	validation_0-rmse:0.74161	validation_1-rmse:0.76113
[47]	validation_0-rmse:0.73874	validation_1-rmse:0.75835
[48]	validation_0-rmse:0.73685	validation_1-rmse:0.75670
[49]	validation_0-rmse:0.72998	validation_1-rmse:0.74995
[50]	validation_0-rmse:0.72732	validation_1-rmse:0.74729
[51]	validation_0-rmse:0.72496	validation_1-rmse:0.74508
[52]	validation_0-rmse:0.72322	validation_1-rmse:0.74357
[53]	validation_0-rmse:0.71648	validation_1-rmse:0.73696
[54]	validation_0-rmse:0.71215	validation_1-rmse:0.73274
[55]	validation_0-rmse:0.70946	validation_1-rmse:0.73034
[56]	validation_0-rmse:0.70474	validation_1-rmse:0.72589
[57]	validation_0-rmse:0.70225	validation_1-rmse:0.72381
[58]	validation_0-rmse:0.69904	validation_1-rmse:0.72064
[59]	validation_0-rmse:0.69714	validation_1-rmse:0.71883
[60]	validation_0-rmse:0.69561	validation_1-rmse:0.71744
[61]	validation_0-rmse:0.69325	validation_1-rmse:0.71524
[62]	validation_0-rmse:0.69104	validation_1-rmse:0.71305
[63]	validation_0-rmse:0.69006	validation_1-rmse:0.71232
[64]	validation_0-rmse:0.68818	validation_1-rmse:0.71033
[65]	validation_0-rmse:0.68616	validation_1-rmse:0.70841
[66]	validation_0-rmse:0.68262	validation_1-rmse:0.70526
[67]	validation_0-rmse:0.68176	validation_1-rmse:0.70447
[68]	validation_0-rmse:0.67850	validation_1-rmse:0.70120
[69]	validation_0-rmse:0.67641	validation_1-rmse:0.69928
[70]	validation_0-rmse:0.67258	validation_1-rmse:0.69567
[71]	validation_0-rmse:0.67036	validation_1-rmse:0.69351
[72]	validation_0-rmse:0.66815	validation_1-rmse:0.69125
[73]	validation_0-rmse:0.66614	validation_1-rmse:0.68928
[74]	validation_0-rmse:0.66432	validation_1-rmse:0.68760
[75]	validation_0-rmse:0.66248	validation_1-rmse:0.68610
[76]	validation_0-rmse:0.65946	validation_1-rmse:0.68334
[77]	validation_0-rmse:0.65517	validation_1-rmse:0.67913
[78]	validation_0-rmse:0.65370	validation_1-rmse:0.67769
[79]	validation_0-rmse:0.65207	validation_1-rmse:0.67613
[80]	validation_0-rmse:0.64983	validation_1-rmse:0.67398
[81]	validation_0-rmse:0.64652	validation_1-rmse:0.67057
[82]	validation_0-rmse:0.64520	validation_1-rmse:0.66954
[83]	validation_0-rmse:0.64431	validation_1-rmse:0.66872
[84]	validation_0-rmse:0.64233	validation_1-rmse:0.66684
[85]	validation_0-rmse:0.63975	validation_1-rmse:0.66435
[86]	validation_0-rmse:0.63599	validation_1-rmse:0.66079
[87]	validation_0-rmse:0.63434	validation_1-rmse:0.65934
[88]	validation_0-rmse:0.63354	validation_1-rmse:0.65869
[89]	validation_0-rmse:0.63196	validation_1-rmse:0.65723
[90]	validation_0-rmse:0.63117	validation_1-rmse:0.65674
[91]	validation_0-rmse:0.62832	validation_1-rmse:0.65397
[92]	validation_0-rmse:0.62586	validation_1-rmse:0.65173
[93]	validation_0-rmse:0.62409	validation_1-rmse:0.65037
[94]	validation_0-rmse:0.62341	validation_1-rmse:0.64970
[95]	validation_0-rmse:0.62245	validation_1-rmse:0.64880
[96]	validation_0-rmse:0.62052	validation_1-rmse:0.64689
[97]	validation_0-rmse:0.61910	validation_1-rmse:0.64546
[98]	validation_0-rmse:0.61797	validation_1-rmse:0.64444
[99]	validation_0-rmse:0.61731	validation_1-rmse:0.64395
[100]	validation_0-rmse:0.61527	validation_1-rmse:0.64188
[101]	validation_0-rmse:0.61252	validation_1-rmse:0.63920
[102]	validation_0-rmse:0.61040	validation_1-rmse:0.63729
[103]	validation_0-rmse:0.60781	validation_1-rmse:0.63474
[104]	validation_0-rmse:0.60627	validation_1-rmse:0.63318
[105]	validation_0-rmse:0.60525	validation_1-rmse:0.63230
[106]	validation_0-rmse:0.60465	validation_1-rmse:0.63181
[107]	validation_0-rmse:0.60387	validation_1-rmse:0.63126
[108]	validation_0-rmse:0.60238	validation_1-rmse:0.62979
[109]	validation_0-rmse:0.60132	validation_1-rmse:0.62888
[110]	validation_0-rmse:0.59925	validation_1-rmse:0.62682
[111]	validation_0-rmse:0.59744	validation_1-rmse:0.62498
[112]	validation_0-rmse:0.59614	validation_1-rmse:0.62372
[113]	validation_0-rmse:0.59443	validation_1-rmse:0.62208
[114]	validation_0-rmse:0.59319	validation_1-rmse:0.62098
[115]	validation_0-rmse:0.59276	validation_1-rmse:0.62057
[116]	validation_0-rmse:0.59202	validation_1-rmse:0.61992
[117]	validation_0-rmse:0.59066	validation_1-rmse:0.61868
[118]	validation_0-rmse:0.58772	validation_1-rmse:0.61593
[119]	validation_0-rmse:0.58679	validation_1-rmse:0.61514
[120]	validation_0-rmse:0.58513	validation_1-rmse:0.61358
[121]	validation_0-rmse:0.58457	validation_1-rmse:0.61312
[122]	validation_0-rmse:0.58340	validation_1-rmse:0.61209
[123]	validation_0-rmse:0.58187	validation_1-rmse:0.61061
[124]	validation_0-rmse:0.58081	validation_1-rmse:0.60963
[125]	validation_0-rmse:0.57979	validation_1-rmse:0.60856
[126]	validation_0-rmse:0.57889	validation_1-rmse:0.60784
[127]	validation_0-rmse:0.57697	validation_1-rmse:0.60598
[128]	validation_0-rmse:0.57600	validation_1-rmse:0.60515
[129]	validation_0-rmse:0.57439	validation_1-rmse:0.60353
[130]	validation_0-rmse:0.57260	validation_1-rmse:0.60175
[131]	validation_0-rmse:0.57148	validation_1-rmse:0.60074
[132]	validation_0-rmse:0.57023	validation_1-rmse:0.59937
[133]	validation_0-rmse:0.56945	validation_1-rmse:0.59871
[134]	validation_0-rmse:0.56755	validation_1-rmse:0.59693
[135]	validation_0-rmse:0.56638	validation_1-rmse:0.59581
[136]	validation_0-rmse:0.56540	validation_1-rmse:0.59494
[137]	validation_0-rmse:0.56342	validation_1-rmse:0.59283
[138]	validation_0-rmse:0.56237	validation_1-rmse:0.59187
[139]	validation_0-rmse:0.56056	validation_1-rmse:0.59030
[140]	validation_0-rmse:0.55908	validation_1-rmse:0.58884
[141]	validation_0-rmse:0.55815	validation_1-rmse:0.58791
[142]	validation_0-rmse:0.55539	validation_1-rmse:0.58534
[143]	validation_0-rmse:0.55494	validation_1-rmse:0.58503
[144]	validation_0-rmse:0.55366	validation_1-rmse:0.58394
[145]	validation_0-rmse:0.55179	validation_1-rmse:0.58223
[146]	validation_0-rmse:0.55087	validation_1-rmse:0.58146
[147]	validation_0-rmse:0.54980	validation_1-rmse:0.58060
[148]	validation_0-rmse:0.54936	validation_1-rmse:0.58024
[149]	validation_0-rmse:0.54801	validation_1-rmse:0.57895
[150]	validation_0-rmse:0.54743	validation_1-rmse:0.57849
[151]	validation_0-rmse:0.54691	validation_1-rmse:0.57810
[152]	validation_0-rmse:0.54565	validation_1-rmse:0.57695
[153]	validation_0-rmse:0.54435	validation_1-rmse:0.57570
[154]	validation_0-rmse:0.54369	validation_1-rmse:0.57506
[155]	validation_0-rmse:0.54322	validation_1-rmse:0.57480
[156]	validation_0-rmse:0.54236	validation_1-rmse:0.57402
[157]	validation_0-rmse:0.54100	validation_1-rmse:0.57250
[158]	validation_0-rmse:0.53975	validation_1-rmse:0.57124
[159]	validation_0-rmse:0.53739	validation_1-rmse:0.56911
[160]	validation_0-rmse:0.53583	validation_1-rmse:0.56773
[161]	validation_0-rmse:0.53499	validation_1-rmse:0.56688
[162]	validation_0-rmse:0.53361	validation_1-rmse:0.56559
[163]	validation_0-rmse:0.53284	validation_1-rmse:0.56496
[164]	validation_0-rmse:0.53238	validation_1-rmse:0.56460
[165]	validation_0-rmse:0.53147	validation_1-rmse:0.56377
[166]	validation_0-rmse:0.53066	validation_1-rmse:0.56313
[167]	validation_0-rmse:0.53004	validation_1-rmse:0.56264
[168]	validation_0-rmse:0.52918	validation_1-rmse:0.56191
[169]	validation_0-rmse:0.52860	validation_1-rmse:0.56138
[170]	validation_0-rmse:0.52758	validation_1-rmse:0.56042
[171]	validation_0-rmse:0.52702	validation_1-rmse:0.56010
[172]	validation_0-rmse:0.52610	validation_1-rmse:0.55931
[173]	validation_0-rmse:0.52566	validation_1-rmse:0.55887
[174]	validation_0-rmse:0.52498	validation_1-rmse:0.55828
[175]	validation_0-rmse:0.52393	validation_1-rmse:0.55722
[176]	validation_0-rmse:0.52274	validation_1-rmse:0.55596
[177]	validation_0-rmse:0.52195	validation_1-rmse:0.55522
[178]	validation_0-rmse:0.52111	validation_1-rmse:0.55448
[179]	validation_0-rmse:0.52027	validation_1-rmse:0.55378
[180]	validation_0-rmse:0.51952	validation_1-rmse:0.55308
[181]	validation_0-rmse:0.51820	validation_1-rmse:0.55167
[182]	validation_0-rmse:0.51790	validation_1-rmse:0.55143
[183]	validation_0-rmse:0.51714	validation_1-rmse:0.55076
[184]	validation_0-rmse:0.51572	validation_1-rmse:0.54942
[185]	validation_0-rmse:0.51481	validation_1-rmse:0.54854
[186]	validation_0-rmse:0.51370	validation_1-rmse:0.54776
[187]	validation_0-rmse:0.51299	validation_1-rmse:0.54706
[188]	validation_0-rmse:0.51258	validation_1-rmse:0.54691
[189]	validation_0-rmse:0.51219	validation_1-rmse:0.54664
[190]	validation_0-rmse:0.51169	validation_1-rmse:0.54624
[191]	validation_0-rmse:0.51034	validation_1-rmse:0.54494
[192]	validation_0-rmse:0.50941	validation_1-rmse:0.54410
[193]	validation_0-rmse:0.50858	validation_1-rmse:0.54330
[194]	validation_0-rmse:0.50823	validation_1-rmse:0.54313
[195]	validation_0-rmse:0.50607	validation_1-rmse:0.54109
[196]	validation_0-rmse:0.50546	validation_1-rmse:0.54068
[197]	validation_0-rmse:0.50382	validation_1-rmse:0.53909
[198]	validation_0-rmse:0.50290	validation_1-rmse:0.53810
[199]	validation_0-rmse:0.50191	validation_1-rmse:0.53701
[200]	validation_0-rmse:0.50105	validation_1-rmse:0.53611
[201]	validation_0-rmse:0.50018	validation_1-rmse:0.53536
[202]	validation_0-rmse:0.49996	validation_1-rmse:0.53517
[203]	validation_0-rmse:0.49947	validation_1-rmse:0.53483
[204]	validation_0-rmse:0.49840	validation_1-rmse:0.53381
[205]	validation_0-rmse:0.49784	validation_1-rmse:0.53337
[206]	validation_0-rmse:0.49677	validation_1-rmse:0.53233
[207]	validation_0-rmse:0.49617	validation_1-rmse:0.53182
[208]	validation_0-rmse:0.49521	validation_1-rmse:0.53093
[209]	validation_0-rmse:0.49405	validation_1-rmse:0.52983
[210]	validation_0-rmse:0.49375	validation_1-rmse:0.52961
[211]	validation_0-rmse:0.49340	validation_1-rmse:0.52934
[212]	validation_0-rmse:0.49237	validation_1-rmse:0.52840
[213]	validation_0-rmse:0.49193	validation_1-rmse:0.52802
[214]	validation_0-rmse:0.49094	validation_1-rmse:0.52707
[215]	validation_0-rmse:0.49013	validation_1-rmse:0.52641
[216]	validation_0-rmse:0.48965	validation_1-rmse:0.52600
[217]	validation_0-rmse:0.48737	validation_1-rmse:0.52383
[218]	validation_0-rmse:0.48710	validation_1-rmse:0.52363
[219]	validation_0-rmse:0.48586	validation_1-rmse:0.52233
[220]	validation_0-rmse:0.48473	validation_1-rmse:0.52122
[221]	validation_0-rmse:0.48404	validation_1-rmse:0.52058
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[780]	validation_0-rmse:0.30028	validation_1-rmse:0.37081
[781]	validation_0-rmse:0.30002	validation_1-rmse:0.37066
[782]	validation_0-rmse:0.29966	validation_1-rmse:0.37034
[783]	validation_0-rmse:0.29950	validation_1-rmse:0.37027
[784]	validation_0-rmse:0.29942	validation_1-rmse:0.37025
[785]	validation_0-rmse:0.29927	validation_1-rmse:0.37011
[786]	validation_0-rmse:0.29908	validation_1-rmse:0.36994
[787]	validation_0-rmse:0.29895	validation_1-rmse:0.36983
[788]	validation_0-rmse:0.29883	validation_1-rmse:0.36978
[789]	validation_0-rmse:0.29863	validation_1-rmse:0.36957
[790]	validation_0-rmse:0.29827	validation_1-rmse:0.36924
[791]	validation_0-rmse:0.29808	validation_1-rmse:0.36911
[792]	validation_0-rmse:0.29772	validation_1-rmse:0.36878
[793]	validation_0-rmse:0.29763	validation_1-rmse:0.36877
[794]	validation_0-rmse:0.29739	validation_1-rmse:0.36861
[795]	validation_0-rmse:0.29724	validation_1-rmse:0.36851
[796]	validation_0-rmse:0.29704	validation_1-rmse:0.36837
[797]	validation_0-rmse:0.29680	validation_1-rmse:0.36814
[798]	validation_0-rmse:0.29676	validation_1-rmse:0.36812
[799]	validation_0-rmse:0.29662	validation_1-rmse:0.36804
[800]	validation_0-rmse:0.29643	validation_1-rmse:0.36790
[801]	validation_0-rmse:0.29633	validation_1-rmse:0.36784
[802]	validation_0-rmse:0.29626	validation_1-rmse:0.36779
[803]	validation_0-rmse:0.29605	validation_1-rmse:0.36767
[804]	validation_0-rmse:0.29581	validation_1-rmse:0.36746
[805]	validation_0-rmse:0.29574	validation_1-rmse:0.36742
[806]	validation_0-rmse:0.29564	validation_1-rmse:0.36734
[807]	validation_0-rmse:0.29557	validation_1-rmse:0.36730
[808]	validation_0-rmse:0.29548	validation_1-rmse:0.36725
[809]	validation_0-rmse:0.29534	validation_1-rmse:0.36712
[810]	validation_0-rmse:0.29510	validation_1-rmse:0.36693
[811]	validation_0-rmse:0.29481	validation_1-rmse:0.36669
[812]	validation_0-rmse:0.29456	validation_1-rmse:0.36644
[813]	validation_0-rmse:0.29434	validation_1-rmse:0.36631
[814]	validation_0-rmse:0.29406	validation_1-rmse:0.36600
[815]	validation_0-rmse:0.29395	validation_1-rmse:0.36590
[816]	validation_0-rmse:0.29380	validation_1-rmse:0.36585
[817]	validation_0-rmse:0.29372	validation_1-rmse:0.36577
[818]	validation_0-rmse:0.29355	validation_1-rmse:0.36564
[819]	validation_0-rmse:0.29348	validation_1-rmse:0.36564
[820]	validation_0-rmse:0.29343	validation_1-rmse:0.36561
[821]	validation_0-rmse:0.29331	validation_1-rmse:0.36557
[822]	validation_0-rmse:0.29315	validation_1-rmse:0.36551
[823]	validation_0-rmse:0.29303	validation_1-rmse:0.36546
[824]	validation_0-rmse:0.29301	validation_1-rmse:0.36547
[825]	validation_0-rmse:0.29282	validation_1-rmse:0.36531
[826]	validation_0-rmse:0.29269	validation_1-rmse:0.36529
[827]	validation_0-rmse:0.29239	validation_1-rmse:0.36500
[828]	validation_0-rmse:0.29231	validation_1-rmse:0.36494
[829]	validation_0-rmse:0.29223	validation_1-rmse:0.36492
[830]	validation_0-rmse:0.29198	validation_1-rmse:0.36471
[831]	validation_0-rmse:0.29179	validation_1-rmse:0.36455
[832]	validation_0-rmse:0.29154	validation_1-rmse:0.36430
[833]	validation_0-rmse:0.29148	validation_1-rmse:0.36431
[834]	validation_0-rmse:0.29134	validation_1-rmse:0.36423
[835]	validation_0-rmse:0.29111	validation_1-rmse:0.36404
[836]	validation_0-rmse:0.29097	validation_1-rmse:0.36402
[837]	validation_0-rmse:0.29074	validation_1-rmse:0.36386
[838]	validation_0-rmse:0.29066	validation_1-rmse:0.36380
[839]	validation_0-rmse:0.29048	validation_1-rmse:0.36366
[840]	validation_0-rmse:0.29038	validation_1-rmse:0.36362
[841]	validation_0-rmse:0.29019	validation_1-rmse:0.36343
[842]	validation_0-rmse:0.28998	validation_1-rmse:0.36329
[843]	validation_0-rmse:0.28979	validation_1-rmse:0.36311
[844]	validation_0-rmse:0.28957	validation_1-rmse:0.36300
[845]	validation_0-rmse:0.28945	validation_1-rmse:0.36290
[846]	validation_0-rmse:0.28933	validation_1-rmse:0.36281
[847]	validation_0-rmse:0.28930	validation_1-rmse:0.36278
[848]	validation_0-rmse:0.28912	validation_1-rmse:0.36261
[849]	validation_0-rmse:0.28899	validation_1-rmse:0.36258
[850]	validation_0-rmse:0.28894	validation_1-rmse:0.36254
[851]	validation_0-rmse:0.28880	validation_1-rmse:0.36248
[852]	validation_0-rmse:0.28869	validation_1-rmse:0.36249
[853]	validation_0-rmse:0.28848	validation_1-rmse:0.36233
[854]	validation_0-rmse:0.28831	validation_1-rmse:0.36220
[855]	validation_0-rmse:0.28814	validation_1-rmse:0.36209
[856]	validation_0-rmse:0.28796	validation_1-rmse:0.36195
[857]	validation_0-rmse:0.28787	validation_1-rmse:0.36195
[858]	validation_0-rmse:0.28772	validation_1-rmse:0.36186
[859]	validation_0-rmse:0.28757	validation_1-rmse:0.36168
[860]	validation_0-rmse:0.28742	validation_1-rmse:0.36162
[861]	validation_0-rmse:0.28725	validation_1-rmse:0.36151
[862]	validation_0-rmse:0.28721	validation_1-rmse:0.36150
[863]	validation_0-rmse:0.28709	validation_1-rmse:0.36139
[864]	validation_0-rmse:0.28698	validation_1-rmse:0.36134
[865]	validation_0-rmse:0.28687	validation_1-rmse:0.36125
[866]	validation_0-rmse:0.28673	validation_1-rmse:0.36116
[867]	validation_0-rmse:0.28666	validation_1-rmse:0.36116
[868]	validation_0-rmse:0.28660	validation_1-rmse:0.36114
[869]	validation_0-rmse:0.28647	validation_1-rmse:0.36107
[870]	validation_0-rmse:0.28626	validation_1-rmse:0.36088
[871]	validation_0-rmse:0.28616	validation_1-rmse:0.36083
[872]	validation_0-rmse:0.28606	validation_1-rmse:0.36081
[873]	validation_0-rmse:0.28590	validation_1-rmse:0.36068
[874]	validation_0-rmse:0.28581	validation_1-rmse:0.36064
[875]	validation_0-rmse:0.28565	validation_1-rmse:0.36052
[876]	validation_0-rmse:0.28555	validation_1-rmse:0.36049
[877]	validation_0-rmse:0.28533	validation_1-rmse:0.36037
[878]	validation_0-rmse:0.28521	validation_1-rmse:0.36029
[879]	validation_0-rmse:0.28508	validation_1-rmse:0.36021
[880]	validation_0-rmse:0.28493	validation_1-rmse:0.36013
[881]	validation_0-rmse:0.28482	validation_1-rmse:0.36009
[882]	validation_0-rmse:0.28465	validation_1-rmse:0.36000
[883]	validation_0-rmse:0.28437	validation_1-rmse:0.35975
[884]	validation_0-rmse:0.28418	validation_1-rmse:0.35958
[885]	validation_0-rmse:0.28409	validation_1-rmse:0.35954
[886]	validation_0-rmse:0.28404	validation_1-rmse:0.35952
[887]	validation_0-rmse:0.28393	validation_1-rmse:0.35945
[888]	validation_0-rmse:0.28380	validation_1-rmse:0.35936
[889]	validation_0-rmse:0.28376	validation_1-rmse:0.35935
[890]	validation_0-rmse:0.28369	validation_1-rmse:0.35932
[891]	validation_0-rmse:0.28366	validation_1-rmse:0.35930
[892]	validation_0-rmse:0.28356	validation_1-rmse:0.35920
[893]	validation_0-rmse:0.28348	validation_1-rmse:0.35918
[894]	validation_0-rmse:0.28327	validation_1-rmse:0.35894
[895]	validation_0-rmse:0.28320	validation_1-rmse:0.35891
[896]	validation_0-rmse:0.28309	validation_1-rmse:0.35883
[897]	validation_0-rmse:0.28291	validation_1-rmse:0.35867
[898]	validation_0-rmse:0.28285	validation_1-rmse:0.35864
[899]	validation_0-rmse:0.28274	validation_1-rmse:0.35857
[900]	validation_0-rmse:0.28270	validation_1-rmse:0.35857
[901]	validation_0-rmse:0.28253	validation_1-rmse:0.35845
[902]	validation_0-rmse:0.28246	validation_1-rmse:0.35845
[903]	validation_0-rmse:0.28226	validation_1-rmse:0.35829
[904]	validation_0-rmse:0.28211	validation_1-rmse:0.35820
[905]	validation_0-rmse:0.28199	validation_1-rmse:0.35808
[906]	validation_0-rmse:0.28189	validation_1-rmse:0.35797
[907]	validation_0-rmse:0.28177	validation_1-rmse:0.35793
[908]	validation_0-rmse:0.28154	validation_1-rmse:0.35779
[909]	validation_0-rmse:0.28137	validation_1-rmse:0.35768
[910]	validation_0-rmse:0.28115	validation_1-rmse:0.35753
[911]	validation_0-rmse:0.28109	validation_1-rmse:0.35749
[912]	validation_0-rmse:0.28101	validation_1-rmse:0.35741
[913]	validation_0-rmse:0.28092	validation_1-rmse:0.35743
[914]	validation_0-rmse:0.28085	validation_1-rmse:0.35740
[915]	validation_0-rmse:0.28083	validation_1-rmse:0.35739
[916]	validation_0-rmse:0.28079	validation_1-rmse:0.35739
[917]	validation_0-rmse:0.28069	validation_1-rmse:0.35733
[918]	validation_0-rmse:0.28059	validation_1-rmse:0.35729
[919]	validation_0-rmse:0.28052	validation_1-rmse:0.35726
[920]	validation_0-rmse:0.28045	validation_1-rmse:0.35725
[921]	validation_0-rmse:0.28030	validation_1-rmse:0.35713
[922]	validation_0-rmse:0.28017	validation_1-rmse:0.35701
[923]	validation_0-rmse:0.27998	validation_1-rmse:0.35688
[924]	validation_0-rmse:0.27990	validation_1-rmse:0.35686
[925]	validation_0-rmse:0.27983	validation_1-rmse:0.35684
[926]	validation_0-rmse:0.27976	validation_1-rmse:0.35683
[927]	validation_0-rmse:0.27961	validation_1-rmse:0.35669
[928]	validation_0-rmse:0.27958	validation_1-rmse:0.35668
[929]	validation_0-rmse:0.27945	validation_1-rmse:0.35660
[930]	validation_0-rmse:0.27930	validation_1-rmse:0.35652
[931]	validation_0-rmse:0.27906	validation_1-rmse:0.35629
[932]	validation_0-rmse:0.27887	validation_1-rmse:0.35619
[933]	validation_0-rmse:0.27872	validation_1-rmse:0.35611
[934]	validation_0-rmse:0.27854	validation_1-rmse:0.35597
[935]	validation_0-rmse:0.27850	validation_1-rmse:0.35596
[936]	validation_0-rmse:0.27842	validation_1-rmse:0.35591
[937]	validation_0-rmse:0.27827	validation_1-rmse:0.35575
[938]	validation_0-rmse:0.27816	validation_1-rmse:0.35569
[939]	validation_0-rmse:0.27806	validation_1-rmse:0.35572
[940]	validation_0-rmse:0.27778	validation_1-rmse:0.35552
[941]	validation_0-rmse:0.27768	validation_1-rmse:0.35546
[942]	validation_0-rmse:0.27761	validation_1-rmse:0.35541
[943]	validation_0-rmse:0.27747	validation_1-rmse:0.35533
[944]	validation_0-rmse:0.27743	validation_1-rmse:0.35532
[945]	validation_0-rmse:0.27731	validation_1-rmse:0.35526
[946]	validation_0-rmse:0.27719	validation_1-rmse:0.35517
[947]	validation_0-rmse:0.27716	validation_1-rmse:0.35516
[948]	validation_0-rmse:0.27706	validation_1-rmse:0.35511
[949]	validation_0-rmse:0.27686	validation_1-rmse:0.35497
[950]	validation_0-rmse:0.27678	validation_1-rmse:0.35495
[951]	validation_0-rmse:0.27662	validation_1-rmse:0.35484
[952]	validation_0-rmse:0.27655	validation_1-rmse:0.35480
[953]	validation_0-rmse:0.27649	validation_1-rmse:0.35476
[954]	validation_0-rmse:0.27640	validation_1-rmse:0.35473
[955]	validation_0-rmse:0.27633	validation_1-rmse:0.35471
[956]	validation_0-rmse:0.27626	validation_1-rmse:0.35473
[957]	validation_0-rmse:0.27619	validation_1-rmse:0.35467
[958]	validation_0-rmse:0.27604	validation_1-rmse:0.35459
[959]	validation_0-rmse:0.27583	validation_1-rmse:0.35444
[960]	validation_0-rmse:0.27582	validation_1-rmse:0.35443
[961]	validation_0-rmse:0.27561	validation_1-rmse:0.35432
[962]	validation_0-rmse:0.27537	validation_1-rmse:0.35414
[963]	validation_0-rmse:0.27531	validation_1-rmse:0.35415
[964]	validation_0-rmse:0.27512	validation_1-rmse:0.35399
[965]	validation_0-rmse:0.27499	validation_1-rmse:0.35390
[966]	validation_0-rmse:0.27489	validation_1-rmse:0.35385
[967]	validation_0-rmse:0.27476	validation_1-rmse:0.35376
[968]	validation_0-rmse:0.27460	validation_1-rmse:0.35366
[969]	validation_0-rmse:0.27444	validation_1-rmse:0.35355
[970]	validation_0-rmse:0.27441	validation_1-rmse:0.35354
[971]	validation_0-rmse:0.27432	validation_1-rmse:0.35348
[972]	validation_0-rmse:0.27426	validation_1-rmse:0.35348
[973]	validation_0-rmse:0.27405	validation_1-rmse:0.35335
[974]	validation_0-rmse:0.27396	validation_1-rmse:0.35334
[975]	validation_0-rmse:0.27374	validation_1-rmse:0.35309
[976]	validation_0-rmse:0.27370	validation_1-rmse:0.35307
[977]	validation_0-rmse:0.27347	validation_1-rmse:0.35287
[978]	validation_0-rmse:0.27339	validation_1-rmse:0.35282
[979]	validation_0-rmse:0.27310	validation_1-rmse:0.35251
[980]	validation_0-rmse:0.27304	validation_1-rmse:0.35248
[981]	validation_0-rmse:0.27295	validation_1-rmse:0.35239
[982]	validation_0-rmse:0.27283	validation_1-rmse:0.35239
[983]	validation_0-rmse:0.27259	validation_1-rmse:0.35218
[984]	validation_0-rmse:0.27240	validation_1-rmse:0.35210
[985]	validation_0-rmse:0.27222	validation_1-rmse:0.35195
[986]	validation_0-rmse:0.27207	validation_1-rmse:0.35182
[987]	validation_0-rmse:0.27203	validation_1-rmse:0.35179
[988]	validation_0-rmse:0.27200	validation_1-rmse:0.35180
[989]	validation_0-rmse:0.27186	validation_1-rmse:0.35172
[990]	validation_0-rmse:0.27177	validation_1-rmse:0.35168
[991]	validation_0-rmse:0.27170	validation_1-rmse:0.35164
[992]	validation_0-rmse:0.27158	validation_1-rmse:0.35154
[993]	validation_0-rmse:0.27153	validation_1-rmse:0.35154
[994]	validation_0-rmse:0.27143	validation_1-rmse:0.35145
[995]	validation_0-rmse:0.27113	validation_1-rmse:0.35122
[996]	validation_0-rmse:0.27095	validation_1-rmse:0.35110
[997]	validation_0-rmse:0.27087	validation_1-rmse:0.35106
[998]	validation_0-rmse:0.27081	validation_1-rmse:0.35104
[999]	validation_0-rmse:0.27067	validation_1-rmse:0.35097
>>> Using model to predict target TS_GF1_0.04_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of TS_GF1_0.04_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: TS_GF1_0.04_1
  > features: 16 ['TA_T1_2_1', '.TA_T1_2_1-5', '.TA_T1_2_1-4', '.TA_T1_2_1-3', '.TA_T1_2_1-2', '.TA_T1_2_1-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 350640 records (with missing)
  > 331134 available records for target and all features (no missing values)
  > training on 248350 records (75.0%) of 248350 features between 2005-09-09 10:15:00 and 2024-12-31 23:15:00
  > testing on 82784 unseen records (25.0%) of TS_GF1_0.04_1 between 2005-09-09 09:45:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (248350 records)
  > the model was tested on test data (82784 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  16
  > names of features used in model:  ['TA_T1_2_1', '.TA_T1_2_1-5', '.TA_T1_2_1-4', '.TA_T1_2_1-3', '.TA_T1_2_1-2', '.TA_T1_2_1-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of TS_GF1_0.04_1 (82784 records).
  > feature importances are showing permutation importances from 10 repeats

               PERM_IMPORTANCE   PERM_SD
.DOY                  0.395893  0.001807
.TA_T1_2_1-5          0.065700  0.000251
.YEARDOY              0.043202  0.000162
.WEEK                 0.039556  0.000177
TA_T1_2_1             0.037562  0.000179
.RECORDNUMBER         0.037157  0.000162
.YEARMONTH            0.035749  0.000180
.YEARWEEK             0.019466  0.000107
.SEASON               0.013684  0.000143
.TA_T1_2_1-3          0.012897  0.000093
.YEAR                 0.012465  0.000066
.TA_T1_2_1-4          0.010632  0.000052
.TA_T1_2_1-2          0.008461  0.000063
.TA_T1_2_1-1          0.008454  0.000060
.HOUR                 0.007350  0.000043
.MONTH                0.000629  0.000007


## MODEL SCORES
  All scores were calculated based on unseen test data (82784 records).
  > MAE:  0.2601240161563333 (mean absolute error)
  > MedAE:  0.19719306516560753 (median absolute error)
  > MSE:  0.12317727164177596 (mean squared error)
  > RMSE:  0.3509661972922406 (root mean squared error)
  > MAXE:  4.028560814412433 (max error)
  > MAPE:  0.047 (mean absolute percentage error)
  > R2:  0.9971875143076765


Gap-filling using final model ...
>>> Using final model on all data to predict target TS_GF1_0.04_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting TS_GF1_0.04_1 ...
>>> Predicting target TS_GF1_0.04_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 19506 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable TS_GF1_0.04_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (19506 values, TS_GF1_0.04_1_gfXG where flag=1) in comparison to observed targets (331134 values, TS_GF1_0.04_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  TS_GF1_0.04_1
- missing records (observed):  19506 (cross-check from flag: 19506)
- target column (gap-filled):  TS_GF1_0.04_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_TS_GF1_0.04_1_gfXG_ISFILLED
  > flag 0 ... observed targets (331134 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (19506 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.DOY', '.TA_T1_2_1-5', '.YEARDOY', '.WEEK', 'TA_T1_2_1', '.RECORDNUMBER', '.YEARMONTH', '.YEARWEEK', '.SEASON', '.TA_T1_2_1-3', '.YEAR', '.TA_T1_2_1-4', '.TA_T1_2_1-2', '.TA_T1_2_1-1', '.HOUR', '.MONTH']
- number of features used in model:  16
- permutation importances were calculated from 10 repeats.

               PERM_IMPORTANCE   PERM_SD
.DOY                  0.395398  0.000607
.TA_T1_2_1-5          0.066163  0.000188
.YEARDOY              0.043318  0.000141
.WEEK                 0.039847  0.000104
TA_T1_2_1             0.038235  0.000162
.RECORDNUMBER         0.037495  0.000046
.YEARMONTH            0.036054  0.000092
.YEARWEEK             0.019466  0.000045
.SEASON               0.013816  0.000104
.TA_T1_2_1-3          0.013220  0.000064
.YEAR                 0.012467  0.000032
.TA_T1_2_1-4          0.010995  0.000049
.TA_T1_2_1-2          0.008683  0.000029
.TA_T1_2_1-1          0.008673  0.000026
.HOUR                 0.007537  0.000037
.MONTH                0.000633  0.000002

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  0.21800864863021835 (mean absolute error)
- MedAE:  0.16619198131469703 (median absolute error)
- MSE:  0.08573985058123688 (mean squared error)
- RMSE:  0.29281367895171306 (root mean squared error)
- MAXE:  4.028560814412433 (max error)
- MAPE:  0.045 (mean absolute percentage error)
- R2:  0.9980459938565359

Fill TS_GF1_0.15_1

TARGET_COL = 'TS_GF1_0.15_1'
TARGET_GAPFILLED_COL = f'{TARGET_COL}_gfXG'
FLAG_GAPFILLED_COL = f'FLAG_{TARGET_GAPFILLED_COL}_ISFILLED'

# Dataframe for gap-filling
_df = pd.DataFrame()
_df[TARGET_COL] = df[TARGET_COL].copy()
_df['TS_GF1_0.04_1_gfXG'] = df['TS_GF1_0.04_1_gfXG'].copy()

# XGBoost
xgb = XGBoostTS(
    input_df=_df,
    target_col=TARGET_COL,
    features_lag=[-10, -1],
    features_lag_exclude_cols=None,
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)
xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()
xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()
results = xgb.gapfilling_df_

# Add results to main data
df = pd.concat([df, results[[TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]]], axis=1)

# Plot
plotdf = df[[TARGET_COL, TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]].copy()
plotdf.plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));
locs = (plotdf.index.year == 2011) & (plotdf.index.month == 8)
plotdf[locs].plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-09-09 10:15:00 and 2024-12-31 23:45:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:4.37232	validation_1-rmse:4.37077
[1]	validation_0-rmse:3.10113	validation_1-rmse:3.10008
[2]	validation_0-rmse:2.21736	validation_1-rmse:2.21684
[3]	validation_0-rmse:1.60544	validation_1-rmse:1.60533
[4]	validation_0-rmse:1.18834	validation_1-rmse:1.18870
[5]	validation_0-rmse:0.90868	validation_1-rmse:0.90974
[6]	validation_0-rmse:0.72410	validation_1-rmse:0.72602
[7]	validation_0-rmse:0.60477	validation_1-rmse:0.60718
[8]	validation_0-rmse:0.52486	validation_1-rmse:0.52731
[9]	validation_0-rmse:0.47675	validation_1-rmse:0.47987
[10]	validation_0-rmse:0.44742	validation_1-rmse:0.45064
[11]	validation_0-rmse:0.42496	validation_1-rmse:0.42844
[12]	validation_0-rmse:0.40834	validation_1-rmse:0.41165
[13]	validation_0-rmse:0.39592	validation_1-rmse:0.39958
[14]	validation_0-rmse:0.38581	validation_1-rmse:0.38956
[15]	validation_0-rmse:0.37783	validation_1-rmse:0.38162
[16]	validation_0-rmse:0.37080	validation_1-rmse:0.37443
[17]	validation_0-rmse:0.36567	validation_1-rmse:0.36924
[18]	validation_0-rmse:0.36188	validation_1-rmse:0.36550
[19]	validation_0-rmse:0.35578	validation_1-rmse:0.35951
[20]	validation_0-rmse:0.35176	validation_1-rmse:0.35565
[21]	validation_0-rmse:0.34971	validation_1-rmse:0.35382
[22]	validation_0-rmse:0.34591	validation_1-rmse:0.34987
[23]	validation_0-rmse:0.34288	validation_1-rmse:0.34673
[24]	validation_0-rmse:0.33998	validation_1-rmse:0.34383
[25]	validation_0-rmse:0.33667	validation_1-rmse:0.34073
[26]	validation_0-rmse:0.33451	validation_1-rmse:0.33877
[27]	validation_0-rmse:0.33256	validation_1-rmse:0.33695
[28]	validation_0-rmse:0.32976	validation_1-rmse:0.33411
[29]	validation_0-rmse:0.32808	validation_1-rmse:0.33246
[30]	validation_0-rmse:0.32616	validation_1-rmse:0.33077
[31]	validation_0-rmse:0.32300	validation_1-rmse:0.32763
[32]	validation_0-rmse:0.32013	validation_1-rmse:0.32497
[33]	validation_0-rmse:0.31653	validation_1-rmse:0.32138
[34]	validation_0-rmse:0.31441	validation_1-rmse:0.31929
[35]	validation_0-rmse:0.31327	validation_1-rmse:0.31814
[36]	validation_0-rmse:0.31116	validation_1-rmse:0.31604
[37]	validation_0-rmse:0.30985	validation_1-rmse:0.31486
[38]	validation_0-rmse:0.30709	validation_1-rmse:0.31209
[39]	validation_0-rmse:0.30597	validation_1-rmse:0.31113
[40]	validation_0-rmse:0.30434	validation_1-rmse:0.30960
[41]	validation_0-rmse:0.30315	validation_1-rmse:0.30844
[42]	validation_0-rmse:0.30093	validation_1-rmse:0.30613
[43]	validation_0-rmse:0.29979	validation_1-rmse:0.30512
[44]	validation_0-rmse:0.29914	validation_1-rmse:0.30451
[45]	validation_0-rmse:0.29665	validation_1-rmse:0.30196
[46]	validation_0-rmse:0.29444	validation_1-rmse:0.29985
[47]	validation_0-rmse:0.29337	validation_1-rmse:0.29885
[48]	validation_0-rmse:0.29182	validation_1-rmse:0.29742
[49]	validation_0-rmse:0.29065	validation_1-rmse:0.29637
[50]	validation_0-rmse:0.28934	validation_1-rmse:0.29508
[51]	validation_0-rmse:0.28796	validation_1-rmse:0.29388
[52]	validation_0-rmse:0.28612	validation_1-rmse:0.29221
[53]	validation_0-rmse:0.28519	validation_1-rmse:0.29135
[54]	validation_0-rmse:0.28443	validation_1-rmse:0.29073
[55]	validation_0-rmse:0.28263	validation_1-rmse:0.28886
[56]	validation_0-rmse:0.28098	validation_1-rmse:0.28726
[57]	validation_0-rmse:0.27992	validation_1-rmse:0.28622
[58]	validation_0-rmse:0.27858	validation_1-rmse:0.28495
[59]	validation_0-rmse:0.27775	validation_1-rmse:0.28418
[60]	validation_0-rmse:0.27636	validation_1-rmse:0.28279
[61]	validation_0-rmse:0.27502	validation_1-rmse:0.28139
[62]	validation_0-rmse:0.27431	validation_1-rmse:0.28073
[63]	validation_0-rmse:0.27280	validation_1-rmse:0.27939
[64]	validation_0-rmse:0.27177	validation_1-rmse:0.27835
[65]	validation_0-rmse:0.27043	validation_1-rmse:0.27713
[66]	validation_0-rmse:0.26922	validation_1-rmse:0.27606
[67]	validation_0-rmse:0.26854	validation_1-rmse:0.27542
[68]	validation_0-rmse:0.26781	validation_1-rmse:0.27475
[69]	validation_0-rmse:0.26686	validation_1-rmse:0.27384
[70]	validation_0-rmse:0.26610	validation_1-rmse:0.27313
[71]	validation_0-rmse:0.26493	validation_1-rmse:0.27198
[72]	validation_0-rmse:0.26402	validation_1-rmse:0.27107
[73]	validation_0-rmse:0.26351	validation_1-rmse:0.27062
[74]	validation_0-rmse:0.26262	validation_1-rmse:0.26981
[75]	validation_0-rmse:0.26157	validation_1-rmse:0.26875
[76]	validation_0-rmse:0.26075	validation_1-rmse:0.26795
[77]	validation_0-rmse:0.26002	validation_1-rmse:0.26722
[78]	validation_0-rmse:0.25870	validation_1-rmse:0.26594
[79]	validation_0-rmse:0.25769	validation_1-rmse:0.26480
[80]	validation_0-rmse:0.25700	validation_1-rmse:0.26421
[81]	validation_0-rmse:0.25629	validation_1-rmse:0.26346
[82]	validation_0-rmse:0.25518	validation_1-rmse:0.26244
[83]	validation_0-rmse:0.25366	validation_1-rmse:0.26103
[84]	validation_0-rmse:0.25342	validation_1-rmse:0.26087
[85]	validation_0-rmse:0.25238	validation_1-rmse:0.25984
[86]	validation_0-rmse:0.25114	validation_1-rmse:0.25863
[87]	validation_0-rmse:0.25022	validation_1-rmse:0.25767
[88]	validation_0-rmse:0.24973	validation_1-rmse:0.25724
[89]	validation_0-rmse:0.24910	validation_1-rmse:0.25663
[90]	validation_0-rmse:0.24842	validation_1-rmse:0.25605
[91]	validation_0-rmse:0.24726	validation_1-rmse:0.25495
[92]	validation_0-rmse:0.24686	validation_1-rmse:0.25461
[93]	validation_0-rmse:0.24595	validation_1-rmse:0.25370
[94]	validation_0-rmse:0.24510	validation_1-rmse:0.25285
[95]	validation_0-rmse:0.24434	validation_1-rmse:0.25218
[96]	validation_0-rmse:0.24382	validation_1-rmse:0.25175
[97]	validation_0-rmse:0.24309	validation_1-rmse:0.25112
[98]	validation_0-rmse:0.24268	validation_1-rmse:0.25085
[99]	validation_0-rmse:0.24154	validation_1-rmse:0.24969
[100]	validation_0-rmse:0.24088	validation_1-rmse:0.24914
[101]	validation_0-rmse:0.23969	validation_1-rmse:0.24804
[102]	validation_0-rmse:0.23885	validation_1-rmse:0.24729
[103]	validation_0-rmse:0.23848	validation_1-rmse:0.24694
[104]	validation_0-rmse:0.23823	validation_1-rmse:0.24669
[105]	validation_0-rmse:0.23729	validation_1-rmse:0.24584
[106]	validation_0-rmse:0.23681	validation_1-rmse:0.24541
[107]	validation_0-rmse:0.23580	validation_1-rmse:0.24454
[108]	validation_0-rmse:0.23497	validation_1-rmse:0.24373
[109]	validation_0-rmse:0.23416	validation_1-rmse:0.24301
[110]	validation_0-rmse:0.23352	validation_1-rmse:0.24237
[111]	validation_0-rmse:0.23308	validation_1-rmse:0.24201
[112]	validation_0-rmse:0.23287	validation_1-rmse:0.24181
[113]	validation_0-rmse:0.23269	validation_1-rmse:0.24169
[114]	validation_0-rmse:0.23185	validation_1-rmse:0.24088
[115]	validation_0-rmse:0.23116	validation_1-rmse:0.24019
[116]	validation_0-rmse:0.23060	validation_1-rmse:0.23975
[117]	validation_0-rmse:0.22970	validation_1-rmse:0.23888
[118]	validation_0-rmse:0.22890	validation_1-rmse:0.23812
[119]	validation_0-rmse:0.22830	validation_1-rmse:0.23758
[120]	validation_0-rmse:0.22805	validation_1-rmse:0.23734
[121]	validation_0-rmse:0.22750	validation_1-rmse:0.23679
[122]	validation_0-rmse:0.22713	validation_1-rmse:0.23648
[123]	validation_0-rmse:0.22619	validation_1-rmse:0.23559
[124]	validation_0-rmse:0.22584	validation_1-rmse:0.23533
[125]	validation_0-rmse:0.22542	validation_1-rmse:0.23496
[126]	validation_0-rmse:0.22494	validation_1-rmse:0.23461
[127]	validation_0-rmse:0.22408	validation_1-rmse:0.23379
[128]	validation_0-rmse:0.22380	validation_1-rmse:0.23359
[129]	validation_0-rmse:0.22346	validation_1-rmse:0.23336
[130]	validation_0-rmse:0.22312	validation_1-rmse:0.23301
[131]	validation_0-rmse:0.22241	validation_1-rmse:0.23241
[132]	validation_0-rmse:0.22186	validation_1-rmse:0.23192
[133]	validation_0-rmse:0.22082	validation_1-rmse:0.23091
[134]	validation_0-rmse:0.22041	validation_1-rmse:0.23057
[135]	validation_0-rmse:0.21983	validation_1-rmse:0.23003
[136]	validation_0-rmse:0.21932	validation_1-rmse:0.22951
[137]	validation_0-rmse:0.21859	validation_1-rmse:0.22884
[138]	validation_0-rmse:0.21788	validation_1-rmse:0.22817
[139]	validation_0-rmse:0.21753	validation_1-rmse:0.22787
[140]	validation_0-rmse:0.21702	validation_1-rmse:0.22741
[141]	validation_0-rmse:0.21637	validation_1-rmse:0.22678
[142]	validation_0-rmse:0.21611	validation_1-rmse:0.22651
[143]	validation_0-rmse:0.21572	validation_1-rmse:0.22619
[144]	validation_0-rmse:0.21492	validation_1-rmse:0.22545
[145]	validation_0-rmse:0.21466	validation_1-rmse:0.22528
[146]	validation_0-rmse:0.21419	validation_1-rmse:0.22493
[147]	validation_0-rmse:0.21362	validation_1-rmse:0.22435
[148]	validation_0-rmse:0.21312	validation_1-rmse:0.22388
[149]	validation_0-rmse:0.21276	validation_1-rmse:0.22365
[150]	validation_0-rmse:0.21238	validation_1-rmse:0.22326
[151]	validation_0-rmse:0.21198	validation_1-rmse:0.22294
[152]	validation_0-rmse:0.21159	validation_1-rmse:0.22258
[153]	validation_0-rmse:0.21147	validation_1-rmse:0.22245
[154]	validation_0-rmse:0.21106	validation_1-rmse:0.22212
[155]	validation_0-rmse:0.21061	validation_1-rmse:0.22170
[156]	validation_0-rmse:0.21041	validation_1-rmse:0.22160
[157]	validation_0-rmse:0.20961	validation_1-rmse:0.22086
[158]	validation_0-rmse:0.20932	validation_1-rmse:0.22058
[159]	validation_0-rmse:0.20907	validation_1-rmse:0.22033
[160]	validation_0-rmse:0.20865	validation_1-rmse:0.22003
[161]	validation_0-rmse:0.20830	validation_1-rmse:0.21973
[162]	validation_0-rmse:0.20750	validation_1-rmse:0.21901
[163]	validation_0-rmse:0.20724	validation_1-rmse:0.21882
[164]	validation_0-rmse:0.20682	validation_1-rmse:0.21844
[165]	validation_0-rmse:0.20645	validation_1-rmse:0.21808
[166]	validation_0-rmse:0.20599	validation_1-rmse:0.21765
[167]	validation_0-rmse:0.20575	validation_1-rmse:0.21752
[168]	validation_0-rmse:0.20533	validation_1-rmse:0.21714
[169]	validation_0-rmse:0.20440	validation_1-rmse:0.21634
[170]	validation_0-rmse:0.20387	validation_1-rmse:0.21582
[171]	validation_0-rmse:0.20339	validation_1-rmse:0.21542
[172]	validation_0-rmse:0.20280	validation_1-rmse:0.21476
[173]	validation_0-rmse:0.20239	validation_1-rmse:0.21440
[174]	validation_0-rmse:0.20198	validation_1-rmse:0.21404
[175]	validation_0-rmse:0.20178	validation_1-rmse:0.21393
[176]	validation_0-rmse:0.20115	validation_1-rmse:0.21332
[177]	validation_0-rmse:0.20093	validation_1-rmse:0.21314
[178]	validation_0-rmse:0.20074	validation_1-rmse:0.21302
[179]	validation_0-rmse:0.20016	validation_1-rmse:0.21243
[180]	validation_0-rmse:0.19979	validation_1-rmse:0.21210
[181]	validation_0-rmse:0.19929	validation_1-rmse:0.21163
[182]	validation_0-rmse:0.19894	validation_1-rmse:0.21134
[183]	validation_0-rmse:0.19842	validation_1-rmse:0.21090
[184]	validation_0-rmse:0.19804	validation_1-rmse:0.21046
[185]	validation_0-rmse:0.19767	validation_1-rmse:0.21016
[186]	validation_0-rmse:0.19701	validation_1-rmse:0.20954
[187]	validation_0-rmse:0.19672	validation_1-rmse:0.20936
[188]	validation_0-rmse:0.19634	validation_1-rmse:0.20899
[189]	validation_0-rmse:0.19595	validation_1-rmse:0.20863
[190]	validation_0-rmse:0.19566	validation_1-rmse:0.20835
[191]	validation_0-rmse:0.19518	validation_1-rmse:0.20792
[192]	validation_0-rmse:0.19479	validation_1-rmse:0.20756
[193]	validation_0-rmse:0.19460	validation_1-rmse:0.20745
[194]	validation_0-rmse:0.19395	validation_1-rmse:0.20686
[195]	validation_0-rmse:0.19368	validation_1-rmse:0.20666
[196]	validation_0-rmse:0.19319	validation_1-rmse:0.20619
[197]	validation_0-rmse:0.19308	validation_1-rmse:0.20611
[198]	validation_0-rmse:0.19279	validation_1-rmse:0.20585
[199]	validation_0-rmse:0.19252	validation_1-rmse:0.20560
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[759]	validation_0-rmse:0.10776	validation_1-rmse:0.13616
[760]	validation_0-rmse:0.10775	validation_1-rmse:0.13615
[761]	validation_0-rmse:0.10767	validation_1-rmse:0.13607
[762]	validation_0-rmse:0.10758	validation_1-rmse:0.13600
[763]	validation_0-rmse:0.10755	validation_1-rmse:0.13600
[764]	validation_0-rmse:0.10749	validation_1-rmse:0.13595
[765]	validation_0-rmse:0.10736	validation_1-rmse:0.13584
[766]	validation_0-rmse:0.10724	validation_1-rmse:0.13574
[767]	validation_0-rmse:0.10718	validation_1-rmse:0.13570
[768]	validation_0-rmse:0.10713	validation_1-rmse:0.13568
[769]	validation_0-rmse:0.10703	validation_1-rmse:0.13562
[770]	validation_0-rmse:0.10700	validation_1-rmse:0.13559
[771]	validation_0-rmse:0.10695	validation_1-rmse:0.13557
[772]	validation_0-rmse:0.10686	validation_1-rmse:0.13551
[773]	validation_0-rmse:0.10679	validation_1-rmse:0.13546
[774]	validation_0-rmse:0.10671	validation_1-rmse:0.13539
[775]	validation_0-rmse:0.10664	validation_1-rmse:0.13533
[776]	validation_0-rmse:0.10660	validation_1-rmse:0.13533
[777]	validation_0-rmse:0.10656	validation_1-rmse:0.13530
[778]	validation_0-rmse:0.10650	validation_1-rmse:0.13529
[779]	validation_0-rmse:0.10645	validation_1-rmse:0.13526
[780]	validation_0-rmse:0.10628	validation_1-rmse:0.13512
[781]	validation_0-rmse:0.10619	validation_1-rmse:0.13504
[782]	validation_0-rmse:0.10614	validation_1-rmse:0.13502
[783]	validation_0-rmse:0.10611	validation_1-rmse:0.13501
[784]	validation_0-rmse:0.10605	validation_1-rmse:0.13498
[785]	validation_0-rmse:0.10598	validation_1-rmse:0.13491
[786]	validation_0-rmse:0.10596	validation_1-rmse:0.13490
[787]	validation_0-rmse:0.10592	validation_1-rmse:0.13487
[788]	validation_0-rmse:0.10588	validation_1-rmse:0.13483
[789]	validation_0-rmse:0.10582	validation_1-rmse:0.13476
[790]	validation_0-rmse:0.10581	validation_1-rmse:0.13475
[791]	validation_0-rmse:0.10568	validation_1-rmse:0.13463
[792]	validation_0-rmse:0.10563	validation_1-rmse:0.13459
[793]	validation_0-rmse:0.10552	validation_1-rmse:0.13450
[794]	validation_0-rmse:0.10546	validation_1-rmse:0.13446
[795]	validation_0-rmse:0.10534	validation_1-rmse:0.13437
[796]	validation_0-rmse:0.10524	validation_1-rmse:0.13431
[797]	validation_0-rmse:0.10519	validation_1-rmse:0.13426
[798]	validation_0-rmse:0.10515	validation_1-rmse:0.13426
[799]	validation_0-rmse:0.10510	validation_1-rmse:0.13422
[800]	validation_0-rmse:0.10500	validation_1-rmse:0.13413
[801]	validation_0-rmse:0.10495	validation_1-rmse:0.13412
[802]	validation_0-rmse:0.10488	validation_1-rmse:0.13406
[803]	validation_0-rmse:0.10483	validation_1-rmse:0.13402
[804]	validation_0-rmse:0.10476	validation_1-rmse:0.13397
[805]	validation_0-rmse:0.10471	validation_1-rmse:0.13395
[806]	validation_0-rmse:0.10464	validation_1-rmse:0.13388
[807]	validation_0-rmse:0.10458	validation_1-rmse:0.13385
[808]	validation_0-rmse:0.10453	validation_1-rmse:0.13383
[809]	validation_0-rmse:0.10443	validation_1-rmse:0.13375
[810]	validation_0-rmse:0.10434	validation_1-rmse:0.13368
[811]	validation_0-rmse:0.10429	validation_1-rmse:0.13367
[812]	validation_0-rmse:0.10422	validation_1-rmse:0.13361
[813]	validation_0-rmse:0.10414	validation_1-rmse:0.13354
[814]	validation_0-rmse:0.10409	validation_1-rmse:0.13351
[815]	validation_0-rmse:0.10404	validation_1-rmse:0.13345
[816]	validation_0-rmse:0.10399	validation_1-rmse:0.13344
[817]	validation_0-rmse:0.10394	validation_1-rmse:0.13340
[818]	validation_0-rmse:0.10389	validation_1-rmse:0.13336
[819]	validation_0-rmse:0.10385	validation_1-rmse:0.13333
[820]	validation_0-rmse:0.10378	validation_1-rmse:0.13329
[821]	validation_0-rmse:0.10375	validation_1-rmse:0.13329
[822]	validation_0-rmse:0.10368	validation_1-rmse:0.13324
[823]	validation_0-rmse:0.10365	validation_1-rmse:0.13323
[824]	validation_0-rmse:0.10363	validation_1-rmse:0.13323
[825]	validation_0-rmse:0.10355	validation_1-rmse:0.13317
[826]	validation_0-rmse:0.10350	validation_1-rmse:0.13315
[827]	validation_0-rmse:0.10340	validation_1-rmse:0.13308
[828]	validation_0-rmse:0.10333	validation_1-rmse:0.13300
[829]	validation_0-rmse:0.10329	validation_1-rmse:0.13299
[830]	validation_0-rmse:0.10323	validation_1-rmse:0.13294
[831]	validation_0-rmse:0.10320	validation_1-rmse:0.13293
[832]	validation_0-rmse:0.10314	validation_1-rmse:0.13289
[833]	validation_0-rmse:0.10311	validation_1-rmse:0.13288
[834]	validation_0-rmse:0.10308	validation_1-rmse:0.13287
[835]	validation_0-rmse:0.10303	validation_1-rmse:0.13283
[836]	validation_0-rmse:0.10292	validation_1-rmse:0.13276
[837]	validation_0-rmse:0.10289	validation_1-rmse:0.13276
[838]	validation_0-rmse:0.10286	validation_1-rmse:0.13274
[839]	validation_0-rmse:0.10278	validation_1-rmse:0.13267
[840]	validation_0-rmse:0.10273	validation_1-rmse:0.13264
[841]	validation_0-rmse:0.10267	validation_1-rmse:0.13261
[842]	validation_0-rmse:0.10256	validation_1-rmse:0.13253
[843]	validation_0-rmse:0.10252	validation_1-rmse:0.13252
[844]	validation_0-rmse:0.10246	validation_1-rmse:0.13247
[845]	validation_0-rmse:0.10242	validation_1-rmse:0.13245
[846]	validation_0-rmse:0.10234	validation_1-rmse:0.13238
[847]	validation_0-rmse:0.10230	validation_1-rmse:0.13237
[848]	validation_0-rmse:0.10227	validation_1-rmse:0.13236
[849]	validation_0-rmse:0.10223	validation_1-rmse:0.13234
[850]	validation_0-rmse:0.10217	validation_1-rmse:0.13229
[851]	validation_0-rmse:0.10214	validation_1-rmse:0.13227
[852]	validation_0-rmse:0.10211	validation_1-rmse:0.13225
[853]	validation_0-rmse:0.10201	validation_1-rmse:0.13218
[854]	validation_0-rmse:0.10193	validation_1-rmse:0.13211
[855]	validation_0-rmse:0.10187	validation_1-rmse:0.13207
[856]	validation_0-rmse:0.10181	validation_1-rmse:0.13206
[857]	validation_0-rmse:0.10175	validation_1-rmse:0.13201
[858]	validation_0-rmse:0.10165	validation_1-rmse:0.13192
[859]	validation_0-rmse:0.10162	validation_1-rmse:0.13191
[860]	validation_0-rmse:0.10158	validation_1-rmse:0.13190
[861]	validation_0-rmse:0.10154	validation_1-rmse:0.13188
[862]	validation_0-rmse:0.10147	validation_1-rmse:0.13183
[863]	validation_0-rmse:0.10142	validation_1-rmse:0.13177
[864]	validation_0-rmse:0.10138	validation_1-rmse:0.13174
[865]	validation_0-rmse:0.10132	validation_1-rmse:0.13170
[866]	validation_0-rmse:0.10130	validation_1-rmse:0.13170
[867]	validation_0-rmse:0.10128	validation_1-rmse:0.13169
[868]	validation_0-rmse:0.10125	validation_1-rmse:0.13165
[869]	validation_0-rmse:0.10120	validation_1-rmse:0.13161
[870]	validation_0-rmse:0.10116	validation_1-rmse:0.13158
[871]	validation_0-rmse:0.10111	validation_1-rmse:0.13155
[872]	validation_0-rmse:0.10108	validation_1-rmse:0.13152
[873]	validation_0-rmse:0.10097	validation_1-rmse:0.13143
[874]	validation_0-rmse:0.10094	validation_1-rmse:0.13143
[875]	validation_0-rmse:0.10084	validation_1-rmse:0.13136
[876]	validation_0-rmse:0.10079	validation_1-rmse:0.13135
[877]	validation_0-rmse:0.10076	validation_1-rmse:0.13135
[878]	validation_0-rmse:0.10071	validation_1-rmse:0.13134
[879]	validation_0-rmse:0.10066	validation_1-rmse:0.13129
[880]	validation_0-rmse:0.10062	validation_1-rmse:0.13128
[881]	validation_0-rmse:0.10056	validation_1-rmse:0.13124
[882]	validation_0-rmse:0.10050	validation_1-rmse:0.13120
[883]	validation_0-rmse:0.10047	validation_1-rmse:0.13118
[884]	validation_0-rmse:0.10040	validation_1-rmse:0.13112
[885]	validation_0-rmse:0.10036	validation_1-rmse:0.13109
[886]	validation_0-rmse:0.10030	validation_1-rmse:0.13104
[887]	validation_0-rmse:0.10029	validation_1-rmse:0.13104
[888]	validation_0-rmse:0.10027	validation_1-rmse:0.13104
[889]	validation_0-rmse:0.10020	validation_1-rmse:0.13098
[890]	validation_0-rmse:0.10019	validation_1-rmse:0.13097
[891]	validation_0-rmse:0.10013	validation_1-rmse:0.13095
[892]	validation_0-rmse:0.10010	validation_1-rmse:0.13095
[893]	validation_0-rmse:0.10004	validation_1-rmse:0.13090
[894]	validation_0-rmse:0.10002	validation_1-rmse:0.13088
[895]	validation_0-rmse:0.09997	validation_1-rmse:0.13086
[896]	validation_0-rmse:0.09992	validation_1-rmse:0.13082
[897]	validation_0-rmse:0.09988	validation_1-rmse:0.13079
[898]	validation_0-rmse:0.09979	validation_1-rmse:0.13070
[899]	validation_0-rmse:0.09966	validation_1-rmse:0.13058
[900]	validation_0-rmse:0.09960	validation_1-rmse:0.13054
[901]	validation_0-rmse:0.09956	validation_1-rmse:0.13054
[902]	validation_0-rmse:0.09954	validation_1-rmse:0.13052
[903]	validation_0-rmse:0.09945	validation_1-rmse:0.13046
[904]	validation_0-rmse:0.09935	validation_1-rmse:0.13037
[905]	validation_0-rmse:0.09930	validation_1-rmse:0.13034
[906]	validation_0-rmse:0.09924	validation_1-rmse:0.13031
[907]	validation_0-rmse:0.09918	validation_1-rmse:0.13026
[908]	validation_0-rmse:0.09910	validation_1-rmse:0.13018
[909]	validation_0-rmse:0.09904	validation_1-rmse:0.13015
[910]	validation_0-rmse:0.09898	validation_1-rmse:0.13012
[911]	validation_0-rmse:0.09893	validation_1-rmse:0.13009
[912]	validation_0-rmse:0.09889	validation_1-rmse:0.13006
[913]	validation_0-rmse:0.09883	validation_1-rmse:0.13002
[914]	validation_0-rmse:0.09881	validation_1-rmse:0.13001
[915]	validation_0-rmse:0.09875	validation_1-rmse:0.12995
[916]	validation_0-rmse:0.09868	validation_1-rmse:0.12990
[917]	validation_0-rmse:0.09864	validation_1-rmse:0.12987
[918]	validation_0-rmse:0.09858	validation_1-rmse:0.12984
[919]	validation_0-rmse:0.09854	validation_1-rmse:0.12982
[920]	validation_0-rmse:0.09852	validation_1-rmse:0.12982
[921]	validation_0-rmse:0.09850	validation_1-rmse:0.12979
[922]	validation_0-rmse:0.09843	validation_1-rmse:0.12973
[923]	validation_0-rmse:0.09838	validation_1-rmse:0.12971
[924]	validation_0-rmse:0.09829	validation_1-rmse:0.12963
[925]	validation_0-rmse:0.09824	validation_1-rmse:0.12958
[926]	validation_0-rmse:0.09815	validation_1-rmse:0.12951
[927]	validation_0-rmse:0.09809	validation_1-rmse:0.12947
[928]	validation_0-rmse:0.09801	validation_1-rmse:0.12942
[929]	validation_0-rmse:0.09798	validation_1-rmse:0.12941
[930]	validation_0-rmse:0.09794	validation_1-rmse:0.12938
[931]	validation_0-rmse:0.09792	validation_1-rmse:0.12939
[932]	validation_0-rmse:0.09784	validation_1-rmse:0.12933
[933]	validation_0-rmse:0.09781	validation_1-rmse:0.12933
[934]	validation_0-rmse:0.09777	validation_1-rmse:0.12931
[935]	validation_0-rmse:0.09775	validation_1-rmse:0.12931
[936]	validation_0-rmse:0.09771	validation_1-rmse:0.12930
[937]	validation_0-rmse:0.09766	validation_1-rmse:0.12928
[938]	validation_0-rmse:0.09760	validation_1-rmse:0.12924
[939]	validation_0-rmse:0.09756	validation_1-rmse:0.12922
[940]	validation_0-rmse:0.09752	validation_1-rmse:0.12919
[941]	validation_0-rmse:0.09745	validation_1-rmse:0.12913
[942]	validation_0-rmse:0.09738	validation_1-rmse:0.12908
[943]	validation_0-rmse:0.09728	validation_1-rmse:0.12901
[944]	validation_0-rmse:0.09722	validation_1-rmse:0.12894
[945]	validation_0-rmse:0.09717	validation_1-rmse:0.12892
[946]	validation_0-rmse:0.09711	validation_1-rmse:0.12889
[947]	validation_0-rmse:0.09705	validation_1-rmse:0.12884
[948]	validation_0-rmse:0.09699	validation_1-rmse:0.12880
[949]	validation_0-rmse:0.09697	validation_1-rmse:0.12880
[950]	validation_0-rmse:0.09695	validation_1-rmse:0.12879
[951]	validation_0-rmse:0.09691	validation_1-rmse:0.12877
[952]	validation_0-rmse:0.09678	validation_1-rmse:0.12865
[953]	validation_0-rmse:0.09672	validation_1-rmse:0.12860
[954]	validation_0-rmse:0.09670	validation_1-rmse:0.12859
[955]	validation_0-rmse:0.09666	validation_1-rmse:0.12856
[956]	validation_0-rmse:0.09663	validation_1-rmse:0.12854
[957]	validation_0-rmse:0.09660	validation_1-rmse:0.12853
[958]	validation_0-rmse:0.09658	validation_1-rmse:0.12852
[959]	validation_0-rmse:0.09652	validation_1-rmse:0.12847
[960]	validation_0-rmse:0.09650	validation_1-rmse:0.12845
[961]	validation_0-rmse:0.09648	validation_1-rmse:0.12845
[962]	validation_0-rmse:0.09641	validation_1-rmse:0.12839
[963]	validation_0-rmse:0.09634	validation_1-rmse:0.12836
[964]	validation_0-rmse:0.09631	validation_1-rmse:0.12835
[965]	validation_0-rmse:0.09623	validation_1-rmse:0.12832
[966]	validation_0-rmse:0.09617	validation_1-rmse:0.12827
[967]	validation_0-rmse:0.09614	validation_1-rmse:0.12825
[968]	validation_0-rmse:0.09610	validation_1-rmse:0.12825
[969]	validation_0-rmse:0.09603	validation_1-rmse:0.12820
[970]	validation_0-rmse:0.09597	validation_1-rmse:0.12815
[971]	validation_0-rmse:0.09591	validation_1-rmse:0.12813
[972]	validation_0-rmse:0.09589	validation_1-rmse:0.12813
[973]	validation_0-rmse:0.09587	validation_1-rmse:0.12812
[974]	validation_0-rmse:0.09576	validation_1-rmse:0.12801
[975]	validation_0-rmse:0.09572	validation_1-rmse:0.12798
[976]	validation_0-rmse:0.09565	validation_1-rmse:0.12795
[977]	validation_0-rmse:0.09559	validation_1-rmse:0.12790
[978]	validation_0-rmse:0.09558	validation_1-rmse:0.12789
[979]	validation_0-rmse:0.09555	validation_1-rmse:0.12788
[980]	validation_0-rmse:0.09550	validation_1-rmse:0.12784
[981]	validation_0-rmse:0.09544	validation_1-rmse:0.12779
[982]	validation_0-rmse:0.09540	validation_1-rmse:0.12776
[983]	validation_0-rmse:0.09533	validation_1-rmse:0.12772
[984]	validation_0-rmse:0.09527	validation_1-rmse:0.12768
[985]	validation_0-rmse:0.09525	validation_1-rmse:0.12767
[986]	validation_0-rmse:0.09520	validation_1-rmse:0.12762
[987]	validation_0-rmse:0.09518	validation_1-rmse:0.12762
[988]	validation_0-rmse:0.09513	validation_1-rmse:0.12758
[989]	validation_0-rmse:0.09509	validation_1-rmse:0.12756
[990]	validation_0-rmse:0.09507	validation_1-rmse:0.12756
[991]	validation_0-rmse:0.09499	validation_1-rmse:0.12749
[992]	validation_0-rmse:0.09494	validation_1-rmse:0.12746
[993]	validation_0-rmse:0.09491	validation_1-rmse:0.12745
[994]	validation_0-rmse:0.09485	validation_1-rmse:0.12740
[995]	validation_0-rmse:0.09482	validation_1-rmse:0.12738
[996]	validation_0-rmse:0.09479	validation_1-rmse:0.12736
[997]	validation_0-rmse:0.09476	validation_1-rmse:0.12736
[998]	validation_0-rmse:0.09470	validation_1-rmse:0.12732
[999]	validation_0-rmse:0.09464	validation_1-rmse:0.12729
>>> Using model to predict target TS_GF1_0.15_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of TS_GF1_0.15_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: TS_GF1_0.15_1
  > features: 21 ['TS_GF1_0.04_1_gfXG', '.TS_GF1_0.04_1_gfXG-10', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-8', '.TS_GF1_0.04_1_gfXG-7', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-5', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-2', '.TS_GF1_0.04_1_gfXG-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 350640 records (with missing)
  > 331848 available records for target and all features (no missing values)
  > training on 248886 records (75.0%) of 248886 features between 2005-09-09 10:15:00 and 2024-12-31 23:45:00
  > testing on 82962 unseen records (25.0%) of TS_GF1_0.15_1 between 2005-09-09 09:45:00 and 2024-12-31 20:15:00

## MODEL
  > the model was trained on training data (248886 records)
  > the model was tested on test data (82962 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  21
  > names of features used in model:  ['TS_GF1_0.04_1_gfXG', '.TS_GF1_0.04_1_gfXG-10', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-8', '.TS_GF1_0.04_1_gfXG-7', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-5', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-2', '.TS_GF1_0.04_1_gfXG-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of TS_GF1_0.15_1 (82962 records).
  > feature importances are showing permutation importances from 10 repeats

                        PERM_IMPORTANCE   PERM_SD
.TS_GF1_0.04_1_gfXG-10         0.171001  0.000591
TS_GF1_0.04_1_gfXG             0.101636  0.000454
.DOY                           0.067057  0.000274
.TS_GF1_0.04_1_gfXG-9          0.038266  0.000191
.TS_GF1_0.04_1_gfXG-7          0.023913  0.000140
.YEARMONTH                     0.009845  0.000084
.YEARDOY                       0.008693  0.000038
.WEEK                          0.005000  0.000030
.RECORDNUMBER                  0.004602  0.000021
.TS_GF1_0.04_1_gfXG-8          0.004187  0.000031
.YEARWEEK                      0.003581  0.000022
.TS_GF1_0.04_1_gfXG-5          0.003025  0.000015
.YEAR                          0.002775  0.000021
.HOUR                          0.002376  0.000020
.TS_GF1_0.04_1_gfXG-3          0.001599  0.000008
.TS_GF1_0.04_1_gfXG-1          0.001250  0.000004
.TS_GF1_0.04_1_gfXG-4          0.001103  0.000004
.TS_GF1_0.04_1_gfXG-6          0.000920  0.000008
.MONTH                         0.000512  0.000004
.TS_GF1_0.04_1_gfXG-2          0.000512  0.000003
.SEASON                        0.000455  0.000006


## MODEL SCORES
  All scores were calculated based on unseen test data (82962 records).
  > MAE:  0.09239192780502478 (mean absolute error)
  > MedAE:  0.06836024127685536 (median absolute error)
  > MSE:  0.016202731346709492 (mean squared error)
  > RMSE:  0.1272899499045761 (root mean squared error)
  > MAXE:  2.227963451794434 (max error)
  > MAPE:  0.011 (mean absolute percentage error)
  > R2:  0.9995774893230212


Gap-filling using final model ...
>>> Using final model on all data to predict target TS_GF1_0.15_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting TS_GF1_0.15_1 ...
>>> Predicting target TS_GF1_0.15_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 18792 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable TS_GF1_0.15_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (18792 values, TS_GF1_0.15_1_gfXG where flag=1) in comparison to observed targets (331848 values, TS_GF1_0.15_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  TS_GF1_0.15_1
- missing records (observed):  18792 (cross-check from flag: 18792)
- target column (gap-filled):  TS_GF1_0.15_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_TS_GF1_0.15_1_gfXG_ISFILLED
  > flag 0 ... observed targets (331848 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (18792 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.TS_GF1_0.04_1_gfXG-10', 'TS_GF1_0.04_1_gfXG', '.DOY', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-7', '.YEARMONTH', '.YEARDOY', '.WEEK', '.RECORDNUMBER', '.TS_GF1_0.04_1_gfXG-8', '.YEARWEEK', '.TS_GF1_0.04_1_gfXG-5', '.YEAR', '.HOUR', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-1', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-2', '.MONTH', '.SEASON']
- number of features used in model:  21
- permutation importances were calculated from 10 repeats.

                        PERM_IMPORTANCE   PERM_SD
.TS_GF1_0.04_1_gfXG-10         0.171312  0.000237
TS_GF1_0.04_1_gfXG             0.101757  0.000137
.DOY                           0.066810  0.000111
.TS_GF1_0.04_1_gfXG-9          0.038233  0.000074
.TS_GF1_0.04_1_gfXG-7          0.023899  0.000045
.YEARMONTH                     0.009844  0.000035
.YEARDOY                       0.008696  0.000021
.WEEK                          0.005006  0.000010
.RECORDNUMBER                  0.004592  0.000009
.TS_GF1_0.04_1_gfXG-8          0.004206  0.000013
.YEARWEEK                      0.003591  0.000011
.TS_GF1_0.04_1_gfXG-5          0.003040  0.000008
.YEAR                          0.002795  0.000012
.HOUR                          0.002393  0.000010
.TS_GF1_0.04_1_gfXG-3          0.001604  0.000003
.TS_GF1_0.04_1_gfXG-1          0.001275  0.000004
.TS_GF1_0.04_1_gfXG-4          0.001113  0.000003
.TS_GF1_0.04_1_gfXG-6          0.000927  0.000002
.TS_GF1_0.04_1_gfXG-2          0.000517  0.000001
.MONTH                         0.000517  0.000001
.SEASON                        0.000452  0.000001

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  0.07666475406991717 (mean absolute error)
- MedAE:  0.05819989577484108 (median absolute error)
- MSE:  0.010768673391665992 (mean squared error)
- RMSE:  0.10377221878550151 (root mean squared error)
- MAXE:  2.227963451794434 (max error)
- MAPE:  0.009 (mean absolute percentage error)
- R2:  0.9997193889979188

Fill TS_GF1_0.4_1

TARGET_COL = 'TS_GF1_0.4_1'
TARGET_GAPFILLED_COL = f'{TARGET_COL}_gfXG'
FLAG_GAPFILLED_COL = f'FLAG_{TARGET_GAPFILLED_COL}_ISFILLED'

# Dataframe for gap-filling
_df = pd.DataFrame()
_df[TARGET_COL] = df[TARGET_COL].copy()
_df['TS_GF1_0.04_1_gfXG'] = df['TS_GF1_0.04_1_gfXG'].copy()
_df['TS_GF1_0.15_1_gfXG'] = df['TS_GF1_0.15_1_gfXG'].copy()

# XGBoost
xgb = XGBoostTS(
    input_df=_df,
    target_col=TARGET_COL,
    features_lag=[-10, -1],
    features_lag_exclude_cols=None,
    perm_n_repeats=10,
    include_timestamp_as_features=True,
    add_continuous_record_number=True,
    n_estimators=1000,
    random_state=42,
    early_stopping_rounds=50,
    n_jobs=-1
)
xgb.trainmodel(showplot_scores=False, showplot_importance=False)
xgb.report_traintest()
xgb.fillgaps(showplot_scores=False, showplot_importance=False)
xgb.report_gapfilling()
results = xgb.gapfilling_df_

# Add results to main data
df = pd.concat([df, results[[TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]]], axis=1)

# Plot
plotdf = df[[TARGET_COL, TARGET_GAPFILLED_COL, FLAG_GAPFILLED_COL]].copy()
plotdf.plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));
locs = (plotdf.index.year == 2011) & (plotdf.index.month == 8)
plotdf[locs].plot(x_compat=True, title=TARGET_COL, subplots=True, figsize=(20, 6));

Adding new data columns ...
++ Added new columns with timestamp info: ['.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK'] 
++ Added new column .RECORDNUMBER with record numbers from 1 to 350640.

Training final model ...
>>> Training model <class 'xgboost.sklearn.XGBRegressor'> based on data between 2005-09-09 10:15:00 and 2024-12-31 23:15:00 ...
>>> Fitting model to training data ...
[0]	validation_0-rmse:3.84965	validation_1-rmse:3.84616
[1]	validation_0-rmse:2.71625	validation_1-rmse:2.71394
[2]	validation_0-rmse:1.92710	validation_1-rmse:1.92541
[3]	validation_0-rmse:1.38134	validation_1-rmse:1.38024
[4]	validation_0-rmse:1.00683	validation_1-rmse:1.00627
[5]	validation_0-rmse:0.75413	validation_1-rmse:0.75427
[6]	validation_0-rmse:0.58644	validation_1-rmse:0.58703
[7]	validation_0-rmse:0.47932	validation_1-rmse:0.48022
[8]	validation_0-rmse:0.40890	validation_1-rmse:0.41008
[9]	validation_0-rmse:0.36666	validation_1-rmse:0.36809
[10]	validation_0-rmse:0.33937	validation_1-rmse:0.34114
[11]	validation_0-rmse:0.31951	validation_1-rmse:0.32166
[12]	validation_0-rmse:0.30753	validation_1-rmse:0.30984
[13]	validation_0-rmse:0.29701	validation_1-rmse:0.29896
[14]	validation_0-rmse:0.29050	validation_1-rmse:0.29268
[15]	validation_0-rmse:0.28485	validation_1-rmse:0.28694
[16]	validation_0-rmse:0.28087	validation_1-rmse:0.28314
[17]	validation_0-rmse:0.27371	validation_1-rmse:0.27598
[18]	validation_0-rmse:0.26941	validation_1-rmse:0.27179
[19]	validation_0-rmse:0.26620	validation_1-rmse:0.26895
[20]	validation_0-rmse:0.26260	validation_1-rmse:0.26511
[21]	validation_0-rmse:0.26061	validation_1-rmse:0.26322
[22]	validation_0-rmse:0.25506	validation_1-rmse:0.25751
[23]	validation_0-rmse:0.25241	validation_1-rmse:0.25503
[24]	validation_0-rmse:0.25013	validation_1-rmse:0.25285
[25]	validation_0-rmse:0.24839	validation_1-rmse:0.25116
[26]	validation_0-rmse:0.24676	validation_1-rmse:0.24965
[27]	validation_0-rmse:0.24433	validation_1-rmse:0.24731
[28]	validation_0-rmse:0.24202	validation_1-rmse:0.24508
[29]	validation_0-rmse:0.24038	validation_1-rmse:0.24338
[30]	validation_0-rmse:0.23776	validation_1-rmse:0.24085
[31]	validation_0-rmse:0.23657	validation_1-rmse:0.23978
[32]	validation_0-rmse:0.23229	validation_1-rmse:0.23552
[33]	validation_0-rmse:0.22959	validation_1-rmse:0.23282
[34]	validation_0-rmse:0.22843	validation_1-rmse:0.23176
[35]	validation_0-rmse:0.22705	validation_1-rmse:0.23043
[36]	validation_0-rmse:0.22511	validation_1-rmse:0.22852
[37]	validation_0-rmse:0.22319	validation_1-rmse:0.22667
[38]	validation_0-rmse:0.22207	validation_1-rmse:0.22559
[39]	validation_0-rmse:0.21847	validation_1-rmse:0.22188
[40]	validation_0-rmse:0.21764	validation_1-rmse:0.22112
[41]	validation_0-rmse:0.21523	validation_1-rmse:0.21891
[42]	validation_0-rmse:0.21402	validation_1-rmse:0.21776
[43]	validation_0-rmse:0.21219	validation_1-rmse:0.21599
[44]	validation_0-rmse:0.21095	validation_1-rmse:0.21487
[45]	validation_0-rmse:0.20984	validation_1-rmse:0.21385
[46]	validation_0-rmse:0.20847	validation_1-rmse:0.21252
[47]	validation_0-rmse:0.20717	validation_1-rmse:0.21131
[48]	validation_0-rmse:0.20599	validation_1-rmse:0.21027
[49]	validation_0-rmse:0.20528	validation_1-rmse:0.20960
[50]	validation_0-rmse:0.20376	validation_1-rmse:0.20822
[51]	validation_0-rmse:0.20227	validation_1-rmse:0.20672
[52]	validation_0-rmse:0.20121	validation_1-rmse:0.20568
[53]	validation_0-rmse:0.20032	validation_1-rmse:0.20485
[54]	validation_0-rmse:0.19823	validation_1-rmse:0.20277
[55]	validation_0-rmse:0.19740	validation_1-rmse:0.20201
[56]	validation_0-rmse:0.19649	validation_1-rmse:0.20117
[57]	validation_0-rmse:0.19554	validation_1-rmse:0.20028
[58]	validation_0-rmse:0.19407	validation_1-rmse:0.19878
[59]	validation_0-rmse:0.19281	validation_1-rmse:0.19747
[60]	validation_0-rmse:0.19149	validation_1-rmse:0.19626
[61]	validation_0-rmse:0.19066	validation_1-rmse:0.19542
[62]	validation_0-rmse:0.18992	validation_1-rmse:0.19476
[63]	validation_0-rmse:0.18843	validation_1-rmse:0.19332
[64]	validation_0-rmse:0.18766	validation_1-rmse:0.19260
[65]	validation_0-rmse:0.18666	validation_1-rmse:0.19164
[66]	validation_0-rmse:0.18583	validation_1-rmse:0.19087
[67]	validation_0-rmse:0.18514	validation_1-rmse:0.19022
[68]	validation_0-rmse:0.18436	validation_1-rmse:0.18946
[69]	validation_0-rmse:0.18347	validation_1-rmse:0.18861
[70]	validation_0-rmse:0.18305	validation_1-rmse:0.18820
[71]	validation_0-rmse:0.18206	validation_1-rmse:0.18727
[72]	validation_0-rmse:0.18139	validation_1-rmse:0.18664
[73]	validation_0-rmse:0.18041	validation_1-rmse:0.18564
[74]	validation_0-rmse:0.17995	validation_1-rmse:0.18519
[75]	validation_0-rmse:0.17947	validation_1-rmse:0.18477
[76]	validation_0-rmse:0.17806	validation_1-rmse:0.18336
[77]	validation_0-rmse:0.17701	validation_1-rmse:0.18230
[78]	validation_0-rmse:0.17645	validation_1-rmse:0.18177
[79]	validation_0-rmse:0.17602	validation_1-rmse:0.18137
[80]	validation_0-rmse:0.17520	validation_1-rmse:0.18062
[81]	validation_0-rmse:0.17457	validation_1-rmse:0.18002
[82]	validation_0-rmse:0.17369	validation_1-rmse:0.17914
[83]	validation_0-rmse:0.17285	validation_1-rmse:0.17836
[84]	validation_0-rmse:0.17209	validation_1-rmse:0.17757
[85]	validation_0-rmse:0.17149	validation_1-rmse:0.17704
[86]	validation_0-rmse:0.17097	validation_1-rmse:0.17656
[87]	validation_0-rmse:0.17023	validation_1-rmse:0.17583
[88]	validation_0-rmse:0.16940	validation_1-rmse:0.17503
[89]	validation_0-rmse:0.16909	validation_1-rmse:0.17479
[90]	validation_0-rmse:0.16806	validation_1-rmse:0.17379
[91]	validation_0-rmse:0.16763	validation_1-rmse:0.17344
[92]	validation_0-rmse:0.16698	validation_1-rmse:0.17284
[93]	validation_0-rmse:0.16636	validation_1-rmse:0.17227
[94]	validation_0-rmse:0.16514	validation_1-rmse:0.17113
[95]	validation_0-rmse:0.16449	validation_1-rmse:0.17046
[96]	validation_0-rmse:0.16406	validation_1-rmse:0.17005
[97]	validation_0-rmse:0.16365	validation_1-rmse:0.16968
[98]	validation_0-rmse:0.16330	validation_1-rmse:0.16935
[99]	validation_0-rmse:0.16276	validation_1-rmse:0.16879
[100]	validation_0-rmse:0.16258	validation_1-rmse:0.16863
[101]	validation_0-rmse:0.16210	validation_1-rmse:0.16816
[102]	validation_0-rmse:0.16183	validation_1-rmse:0.16793
[103]	validation_0-rmse:0.16129	validation_1-rmse:0.16744
[104]	validation_0-rmse:0.16047	validation_1-rmse:0.16669
[105]	validation_0-rmse:0.15992	validation_1-rmse:0.16617
[106]	validation_0-rmse:0.15907	validation_1-rmse:0.16535
[107]	validation_0-rmse:0.15860	validation_1-rmse:0.16496
[108]	validation_0-rmse:0.15828	validation_1-rmse:0.16466
[109]	validation_0-rmse:0.15797	validation_1-rmse:0.16438
[110]	validation_0-rmse:0.15731	validation_1-rmse:0.16375
[111]	validation_0-rmse:0.15683	validation_1-rmse:0.16326
[112]	validation_0-rmse:0.15637	validation_1-rmse:0.16287
[113]	validation_0-rmse:0.15557	validation_1-rmse:0.16211
[114]	validation_0-rmse:0.15540	validation_1-rmse:0.16197
[115]	validation_0-rmse:0.15503	validation_1-rmse:0.16164
[116]	validation_0-rmse:0.15427	validation_1-rmse:0.16091
[117]	validation_0-rmse:0.15391	validation_1-rmse:0.16060
[118]	validation_0-rmse:0.15293	validation_1-rmse:0.15966
[119]	validation_0-rmse:0.15257	validation_1-rmse:0.15929
[120]	validation_0-rmse:0.15229	validation_1-rmse:0.15903
[121]	validation_0-rmse:0.15191	validation_1-rmse:0.15867
[122]	validation_0-rmse:0.15157	validation_1-rmse:0.15841
[123]	validation_0-rmse:0.15101	validation_1-rmse:0.15792
[124]	validation_0-rmse:0.15050	validation_1-rmse:0.15742
[125]	validation_0-rmse:0.15013	validation_1-rmse:0.15708
[126]	validation_0-rmse:0.14968	validation_1-rmse:0.15666
[127]	validation_0-rmse:0.14920	validation_1-rmse:0.15618
[128]	validation_0-rmse:0.14860	validation_1-rmse:0.15557
[129]	validation_0-rmse:0.14829	validation_1-rmse:0.15527
[130]	validation_0-rmse:0.14793	validation_1-rmse:0.15488
[131]	validation_0-rmse:0.14751	validation_1-rmse:0.15447
[132]	validation_0-rmse:0.14700	validation_1-rmse:0.15397
[133]	validation_0-rmse:0.14633	validation_1-rmse:0.15333
[134]	validation_0-rmse:0.14588	validation_1-rmse:0.15289
[135]	validation_0-rmse:0.14546	validation_1-rmse:0.15250
[136]	validation_0-rmse:0.14527	validation_1-rmse:0.15233
[137]	validation_0-rmse:0.14483	validation_1-rmse:0.15192
[138]	validation_0-rmse:0.14450	validation_1-rmse:0.15163
[139]	validation_0-rmse:0.14356	validation_1-rmse:0.15073
[140]	validation_0-rmse:0.14324	validation_1-rmse:0.15042
[141]	validation_0-rmse:0.14304	validation_1-rmse:0.15023
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[975]	validation_0-rmse:0.05747	validation_1-rmse:0.07680
[976]	validation_0-rmse:0.05745	validation_1-rmse:0.07680
[977]	validation_0-rmse:0.05742	validation_1-rmse:0.07677
[978]	validation_0-rmse:0.05738	validation_1-rmse:0.07676
[979]	validation_0-rmse:0.05734	validation_1-rmse:0.07673
[980]	validation_0-rmse:0.05732	validation_1-rmse:0.07672
[981]	validation_0-rmse:0.05728	validation_1-rmse:0.07670
[982]	validation_0-rmse:0.05726	validation_1-rmse:0.07669
[983]	validation_0-rmse:0.05726	validation_1-rmse:0.07669
[984]	validation_0-rmse:0.05723	validation_1-rmse:0.07666
[985]	validation_0-rmse:0.05719	validation_1-rmse:0.07664
[986]	validation_0-rmse:0.05716	validation_1-rmse:0.07662
[987]	validation_0-rmse:0.05713	validation_1-rmse:0.07660
[988]	validation_0-rmse:0.05709	validation_1-rmse:0.07657
[989]	validation_0-rmse:0.05706	validation_1-rmse:0.07656
[990]	validation_0-rmse:0.05703	validation_1-rmse:0.07653
[991]	validation_0-rmse:0.05700	validation_1-rmse:0.07652
[992]	validation_0-rmse:0.05696	validation_1-rmse:0.07648
[993]	validation_0-rmse:0.05693	validation_1-rmse:0.07646
[994]	validation_0-rmse:0.05690	validation_1-rmse:0.07646
[995]	validation_0-rmse:0.05686	validation_1-rmse:0.07642
[996]	validation_0-rmse:0.05683	validation_1-rmse:0.07640
[997]	validation_0-rmse:0.05681	validation_1-rmse:0.07639
[998]	validation_0-rmse:0.05677	validation_1-rmse:0.07637
[999]	validation_0-rmse:0.05674	validation_1-rmse:0.07635
>>> Using model to predict target TS_GF1_0.4_1 in unseen test data ...
>>> Using model to calculate permutation importance based on unseen test data ...
>>> Calculating prediction scores based on predicting unseen test data of TS_GF1_0.4_1 ...
>>> Collecting results, details about training and testing can be accessed by calling .report_traintest().
>>> Done.

================================
MODEL TRAINING & TESTING RESULTS
================================

## DATA
  > target: TS_GF1_0.4_1
  > features: 32 ['TS_GF1_0.04_1_gfXG', 'TS_GF1_0.15_1_gfXG', '.TS_GF1_0.04_1_gfXG-10', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-8', '.TS_GF1_0.04_1_gfXG-7', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-5', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-2', '.TS_GF1_0.04_1_gfXG-1', '.TS_GF1_0.15_1_gfXG-10', '.TS_GF1_0.15_1_gfXG-9', '.TS_GF1_0.15_1_gfXG-8', '.TS_GF1_0.15_1_gfXG-7', '.TS_GF1_0.15_1_gfXG-6', '.TS_GF1_0.15_1_gfXG-5', '.TS_GF1_0.15_1_gfXG-4', '.TS_GF1_0.15_1_gfXG-3', '.TS_GF1_0.15_1_gfXG-2', '.TS_GF1_0.15_1_gfXG-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']
  > 350640 records (with missing)
  > 331945 available records for target and all features (no missing values)
  > training on 248958 records (75.0%) of 248958 features between 2005-09-09 10:15:00 and 2024-12-31 23:15:00
  > testing on 82987 unseen records (25.0%) of TS_GF1_0.4_1 between 2005-09-09 09:45:00 and 2024-12-31 23:45:00

## MODEL
  > the model was trained on training data (248958 records)
  > the model was tested on test data (82987 values)
  > estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
  > parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}
  > number of features used in model:  32
  > names of features used in model:  ['TS_GF1_0.04_1_gfXG', 'TS_GF1_0.15_1_gfXG', '.TS_GF1_0.04_1_gfXG-10', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-8', '.TS_GF1_0.04_1_gfXG-7', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-5', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-2', '.TS_GF1_0.04_1_gfXG-1', '.TS_GF1_0.15_1_gfXG-10', '.TS_GF1_0.15_1_gfXG-9', '.TS_GF1_0.15_1_gfXG-8', '.TS_GF1_0.15_1_gfXG-7', '.TS_GF1_0.15_1_gfXG-6', '.TS_GF1_0.15_1_gfXG-5', '.TS_GF1_0.15_1_gfXG-4', '.TS_GF1_0.15_1_gfXG-3', '.TS_GF1_0.15_1_gfXG-2', '.TS_GF1_0.15_1_gfXG-1', '.YEAR', '.SEASON', '.MONTH', '.WEEK', '.DOY', '.HOUR', '.YEARMONTH', '.YEARDOY', '.YEARWEEK', '.RECORDNUMBER']

## FEATURE IMPORTANCES
  > feature importances were calculated based on unseen test data of TS_GF1_0.4_1 (82987 records).
  > feature importances are showing permutation importances from 10 repeats

                        PERM_IMPORTANCE       PERM_SD
.TS_GF1_0.15_1_gfXG-10         0.910924  4.191212e-03
TS_GF1_0.15_1_gfXG             0.117828  5.271950e-04
.TS_GF1_0.04_1_gfXG-10         0.051647  1.730533e-04
.DOY                           0.037540  1.830625e-04
.TS_GF1_0.15_1_gfXG-1          0.012493  5.285167e-05
.YEARMONTH                     0.006508  3.958246e-05
.YEARDOY                       0.005243  3.074491e-05
.WEEK                          0.003683  2.900322e-05
.TS_GF1_0.15_1_gfXG-3          0.002831  1.271613e-05
.TS_GF1_0.15_1_gfXG-2          0.002777  1.054393e-05
.RECORDNUMBER                  0.002218  1.132744e-05
.TS_GF1_0.15_1_gfXG-6          0.002186  1.055718e-05
.TS_GF1_0.15_1_gfXG-9          0.002062  1.302195e-05
.TS_GF1_0.15_1_gfXG-8          0.001934  8.811151e-06
.TS_GF1_0.15_1_gfXG-5          0.001931  1.072211e-05
.YEARWEEK                      0.001765  7.576100e-06
.TS_GF1_0.15_1_gfXG-7          0.001140  6.361922e-06
.TS_GF1_0.15_1_gfXG-4          0.000874  5.121036e-06
TS_GF1_0.04_1_gfXG             0.000868  4.284329e-06
.HOUR                          0.000824  4.861549e-06
.YEAR                          0.000561  2.490840e-06
.SEASON                        0.000443  4.675602e-06
.MONTH                         0.000366  2.901963e-06
.TS_GF1_0.04_1_gfXG-9          0.000239  1.698292e-06
.TS_GF1_0.04_1_gfXG-3          0.000204  1.436919e-06
.TS_GF1_0.04_1_gfXG-6          0.000183  1.244100e-06
.TS_GF1_0.04_1_gfXG-8          0.000160  9.941143e-07
.TS_GF1_0.04_1_gfXG-7          0.000129  9.349745e-07
.TS_GF1_0.04_1_gfXG-5          0.000129  1.047667e-06
.TS_GF1_0.04_1_gfXG-1          0.000112  1.217784e-06
.TS_GF1_0.04_1_gfXG-4          0.000112  4.794149e-07
.TS_GF1_0.04_1_gfXG-2          0.000075  4.830482e-07


## MODEL SCORES
  All scores were calculated based on unseen test data (82987 records).
  > MAE:  0.05732237380665449 (mean absolute error)
  > MedAE:  0.04440019134521478 (median absolute error)
  > MSE:  0.005829624050677381 (mean squared error)
  > RMSE:  0.07635197476606208 (root mean squared error)
  > MAXE:  1.4030933332714852 (max error)
  > MAPE:  0.006 (mean absolute percentage error)
  > R2:  0.9998051035936195


Gap-filling using final model ...
>>> Using final model on all data to predict target TS_GF1_0.4_1 ...
>>> Using final model on all data to calculate permutation importance ...
>>> Calculating prediction scores based on all data predicting TS_GF1_0.4_1 ...
>>> Predicting target TS_GF1_0.4_1 where all features are available ... predicted 350640 records.
>>> Collecting results for final model ...
>>> Filling 18695 missing records in target with predictions from final model ...
>>> Storing gap-filled time series in variable TS_GF1_0.4_1_gfXG ...
>>> Restoring original timestamp in results ...
>>> Combining predictions from full model and fallback model ...

===================
GAP-FILLING RESULTS
===================

Model scores and feature importances were calculated from high-quality predicted targets (18695 values, TS_GF1_0.4_1_gfXG where flag=1) in comparison to observed targets (331945 values, TS_GF1_0.4_1).

## TARGET
- first timestamp:  2005-01-01 00:15:00
- last timestamp:  2024-12-31 23:45:00
- potential number of values: 350640 values)
- target column (observed):  TS_GF1_0.4_1
- missing records (observed):  18695 (cross-check from flag: 18695)
- target column (gap-filled):  TS_GF1_0.4_1_gfXG  (350640 values)
- missing records (gap-filled):  0
- gap-filling flag: FLAG_TS_GF1_0.4_1_gfXG_ISFILLED
  > flag 0 ... observed targets (331945 values)
  > flag 1 ... targets gap-filled with high-quality, all features available (18695 values)
  > flag 2 ... targets gap-filled with fallback (0 values)

## FEATURE IMPORTANCES
- names of features used in model:  ['.TS_GF1_0.15_1_gfXG-10', 'TS_GF1_0.15_1_gfXG', '.TS_GF1_0.04_1_gfXG-10', '.DOY', '.TS_GF1_0.15_1_gfXG-1', '.YEARMONTH', '.YEARDOY', '.WEEK', '.TS_GF1_0.15_1_gfXG-3', '.TS_GF1_0.15_1_gfXG-2', '.RECORDNUMBER', '.TS_GF1_0.15_1_gfXG-6', '.TS_GF1_0.15_1_gfXG-9', '.TS_GF1_0.15_1_gfXG-8', '.TS_GF1_0.15_1_gfXG-5', '.YEARWEEK', '.TS_GF1_0.15_1_gfXG-7', 'TS_GF1_0.04_1_gfXG', '.TS_GF1_0.15_1_gfXG-4', '.HOUR', '.YEAR', '.SEASON', '.MONTH', '.TS_GF1_0.04_1_gfXG-9', '.TS_GF1_0.04_1_gfXG-3', '.TS_GF1_0.04_1_gfXG-6', '.TS_GF1_0.04_1_gfXG-8', '.TS_GF1_0.04_1_gfXG-7', '.TS_GF1_0.04_1_gfXG-5', '.TS_GF1_0.04_1_gfXG-4', '.TS_GF1_0.04_1_gfXG-1', '.TS_GF1_0.04_1_gfXG-2']
- number of features used in model:  32
- permutation importances were calculated from 10 repeats.

                        PERM_IMPORTANCE       PERM_SD
.TS_GF1_0.15_1_gfXG-10         0.913582  1.508894e-03
TS_GF1_0.15_1_gfXG             0.117840  1.833860e-04
.TS_GF1_0.04_1_gfXG-10         0.051775  7.082590e-05
.DOY                           0.037550  6.256391e-05
.TS_GF1_0.15_1_gfXG-1          0.012480  1.998030e-05
.YEARMONTH                     0.006545  1.941035e-05
.YEARDOY                       0.005267  1.292859e-05
.WEEK                          0.003640  1.163480e-05
.TS_GF1_0.15_1_gfXG-3          0.002829  5.248056e-06
.TS_GF1_0.15_1_gfXG-2          0.002780  4.424003e-06
.RECORDNUMBER                  0.002217  5.748742e-06
.TS_GF1_0.15_1_gfXG-6          0.002181  4.479564e-06
.TS_GF1_0.15_1_gfXG-9          0.002068  1.783811e-06
.TS_GF1_0.15_1_gfXG-8          0.001937  4.110727e-06
.TS_GF1_0.15_1_gfXG-5          0.001922  3.467003e-06
.YEARWEEK                      0.001755  3.503844e-06
.TS_GF1_0.15_1_gfXG-7          0.001141  1.961019e-06
TS_GF1_0.04_1_gfXG             0.000875  2.966801e-06
.TS_GF1_0.15_1_gfXG-4          0.000874  2.345641e-06
.HOUR                          0.000840  3.242577e-06
.YEAR                          0.000562  1.345561e-06
.SEASON                        0.000438  2.682111e-06
.MONTH                         0.000365  1.564226e-06
.TS_GF1_0.04_1_gfXG-9          0.000241  4.293217e-07
.TS_GF1_0.04_1_gfXG-3          0.000207  1.252524e-06
.TS_GF1_0.04_1_gfXG-6          0.000184  5.899445e-07
.TS_GF1_0.04_1_gfXG-8          0.000161  5.342488e-07
.TS_GF1_0.04_1_gfXG-7          0.000130  3.113134e-07
.TS_GF1_0.04_1_gfXG-5          0.000129  5.911704e-07
.TS_GF1_0.04_1_gfXG-4          0.000113  4.935562e-07
.TS_GF1_0.04_1_gfXG-1          0.000113  3.051964e-07
.TS_GF1_0.04_1_gfXG-2          0.000077  2.370518e-07

## MODEL
The model was trained on a training set with test size 25.00%.
- estimator:  XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=50,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=1000, n_jobs=-1,
             num_parallel_tree=None, random_state=42, ...)
- parameters:  {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': 50, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1000, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}

## MODEL SCORES
- MAE:  0.04700369088303114 (mean absolute error)
- MedAE:  0.03675746517089884 (median absolute error)
- MSE:  0.0038719013488385883 (mean squared error)
- RMSE:  0.06222460404726243 (root mean squared error)
- MAXE:  1.4030933332714852 (max error)
- MAPE:  0.005 (mean absolute percentage error)
- R2:  0.9998707183040998

Plot

df.plot(x_compat=True, subplots=True, figsize=(20, 14));

Save to file

OUTNAME = "17.3_CH-CHA_meteo10_2005-2024"
filepath = save_parquet(filename=OUTNAME, data=df)
# df.to_csv(f"{OUTNAME}.csv")

Saved file 17.3_CH-CHA_meteo10_2005-2024.parquet (0.406 seconds).

End of notebook.

dt_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f"Finished. {dt_string}")

Finished. 2025-01-21 00:00:53