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REQUEST_DETAILS {'CSVFile': 'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/AirPassengers.csv', 'Present': '', 'DateFormat': None, 'SignalVar': 'AirPassengers', 'TimeVar': 'time', 'Name': 'model_air_by_tests', 'Horizon': 7} | ||
DATASET_DETECTED_COLUMNS Index(['Unnamed: 0', 'time', 'AirPassengers'], dtype='object') | ||
DATASET_FINAL_COLUMNS Index(['Unnamed: 0', 'time', 'AirPassengers'], dtype='object') | ||
TRAIN_PARAMS (144, 3) Index(['Unnamed: 0', 'time', 'AirPassengers'], dtype='object') time AirPassengers 7 | ||
Transformation Model \ | ||
0 _AirPassengers _AirPassengers_ConstantTrend_residue_bestCycle... | ||
1 _AirPassengers _AirPassengers_LinearTrend_residue_bestCycle_b... | ||
2 _AirPassengers _AirPassengers_Lag1Trend_residue_bestCycle_byL... | ||
3 _AirPassengers _AirPassengers_Lag1Trend_residue_bestCycle_byL... | ||
4 _AirPassengers _AirPassengers_PolyTrend_residue_bestCycle_byL... | ||
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Complexity FitCount FitL2 FitMAPE ForecastCount ForecastL2 \ | ||
0 15 109 11.699307 0.0452 28 25.606519 | ||
1 31 109 10.412703 0.0385 28 26.271869 | ||
2 47 109 10.578571 0.0384 28 26.439472 | ||
3 40 109 11.462370 0.0431 28 28.162380 | ||
4 31 109 9.881837 0.0365 28 31.384612 | ||
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ForecastMAPE TestCount TestL2 TestMAPE | ||
0 0.0463 7 35.417653 0.0592 | ||
1 0.0483 7 34.641257 0.0561 | ||
2 0.0493 7 34.575976 0.0508 | ||
3 0.0543 7 40.306826 0.0699 | ||
4 0.0616 7 41.393079 0.0737 | ||
Forecast Columns Index(['AirPassengers', '_AirPassengers', 'time', 'row_number', | ||
'time_Normalized', '_AirPassengers_ConstantTrend', | ||
'_AirPassengers_ConstantTrend_residue', 'cycle_internal', | ||
'_AirPassengers_ConstantTrend_residue_bestCycle_byL2', | ||
'_AirPassengers_ConstantTrend_residue_bestCycle_byL2_residue', | ||
'_AirPassengers_ConstantTrend_residue_bestCycle_byL2_residue_AR(7)', | ||
'_AirPassengers_ConstantTrend_residue_bestCycle_byL2_residue_AR(7)_residue', | ||
'_AirPassengers_Trend', '_AirPassengers_Trend_residue', | ||
'_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', | ||
'_AirPassengers_AR', '_AirPassengers_AR_residue', | ||
'_AirPassengers_TransformedForecast', | ||
'_AirPassengers_TransformedResidue', 'AirPassengers_Forecast', | ||
'AirPassengers_Residue', 'AirPassengers_Forecast_Lower_Bound', | ||
'AirPassengers_Forecast_Upper_Bound'], | ||
dtype='object') | ||
<class 'pandas.core.frame.DataFrame'> | ||
RangeIndex: 151 entries, 0 to 150 | ||
Data columns (total 3 columns): | ||
time 151 non-null float64 | ||
AirPassengers 144 non-null float64 | ||
AirPassengers_Forecast 151 non-null float64 | ||
dtypes: float64(3) | ||
memory usage: 3.6 KB | ||
None | ||
Forecasts | ||
time AirPassengers AirPassengers_Forecast | ||
144 1961.000000 NaN 447.121550 | ||
145 1961.083333 NaN 451.204820 | ||
146 1961.166667 NaN 503.077824 | ||
147 1961.250000 NaN 500.788293 | ||
148 1961.333333 NaN 500.554852 | ||
149 1961.416667 NaN 527.111316 | ||
150 1961.500000 NaN 550.930383 | ||
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<ModelInfo> | ||
{ | ||
"Dataset": { | ||
"Signal": "AirPassengers", | ||
"Time": { | ||
"Horizon": 7, | ||
"TimeMinMax": [ | ||
"1949.0", | ||
"1960.91666667" | ||
], | ||
"TimeVariable": "time" | ||
}, | ||
"Training_Signal_Length": 144 | ||
}, | ||
"Model": { | ||
"AR_Model": "AR(7)", | ||
"Best_Decomposition": "_AirPassengers_ConstantTrend_residue_bestCycle_byL2_residue_AR(7)", | ||
"Cycle": "Cycle_Length_12", | ||
"Signal_Transoformation": "None", | ||
"Trend": "ConstantTrend" | ||
}, | ||
"Model_Performance": { | ||
"COMPLEXITY": "15", | ||
"MAPE": "0.0463", | ||
"RMSE": "25.6065186111" | ||
} | ||
} | ||
</ModelInfo> | ||
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<Forecast> | ||
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</Forecast> | ||
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3.66user 0.08system 0:04.26elapsed 87%CPU (0avgtext+0avgdata 88400maxresident)k | ||
0inputs+64outputs (0major+28089minor)pagefaults 0swaps |
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