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Create a Heroku App #20
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Added these referneces.
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@antoinecarme
antoinecarme committed Jan 9, 2017
1 parent 575754b commit e82d8ae
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2 changes: 2 additions & 0 deletions tests/references/heroku_build_generic_heroku_model.log
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0.03user 0.00system 0:00.03elapsed 97%CPU (0avgtext+0avgdata 9512maxresident)k
0inputs+0outputs (0major+1161minor)pagefaults 0swaps
98 changes: 98 additions & 0 deletions tests/references/heroku_test_air_passengers_heroku.log
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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...

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

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


<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>




<Forecast>
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</Forecast>


3.66user 0.08system 0:04.26elapsed 87%CPU (0avgtext+0avgdata 88400maxresident)k
0inputs+64outputs (0major+28089minor)pagefaults 0swaps
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