def test_air_passengers_missing_data(iTimeMissingDataImputation,
iSignalMissingDataImputation):
b1 = tsds.load_airline_passengers()
df = b1.mPastData
if (iTimeMissingDataImputation is not None):
df = add_some_missing_data_in_time(df, b1.mTimeVar)
if (iSignalMissingDataImputation is not None):
df = add_some_missing_data_in_signal(df, b1.mSignalVar)
lEngine = autof.cForecastEngine()
H = b1.mHorizon
lEngine.mOptions.mMissingDataOptions.mTimeMissingDataImputation = iTimeMissingDataImputation
lEngine.mOptions.mMissingDataOptions.mSignalMissingDataImputation = iSignalMissingDataImputation
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine.getModelInfo()
print(lEngine.mSignalDecomposition.mTrPerfDetails.head())
dfapp_in = df.copy()
dfapp_in.tail()
dfapp_out = lEngine.forecast(dfapp_in, H)
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, b1.mSignalVar + '_Forecast'
]]
print(Forecast_DF.info())
print("Forecasts\n", Forecast_DF.tail(H))
print("\n\n<ModelInfo>")
print(lEngine.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
b1 = tsds.load_airline_passengers()
df = b1.mPastData
df.head()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mParallelMode = True;
lEngine.mOptions.set_active_autoregressions(['SVR']);
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine.standrdPlots(name = "outputs/my_airline_passengers")
dfapp_in = df.copy();
def buildModel(iParallel=True):
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import logging
import logging.config
# logging.config.fileConfig('logging.conf')
logging.basicConfig(level=logging.INFO)
b1 = tsds.load_airline_passengers()
df = b1.mPastData
df.head()
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon
# lEngine.mOptions.enable_slow_mode();
lEngine.mOptions.mEnableSeasonals = True
lEngine.mOptions.mEnableCycles = True
lEngine.mOptions.mDebugPerformance = True
lEngine.mOptions.mParallelMode = iParallel
lEngine.mOptions.set_active_autoregressions(['MLP', 'LSTM'])
# lEngine.mOptions.mMaxAROrder = 2;
lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)
lEngine2 = pickleModel(lModel)
lEngine2.getModelInfo()
print(lEngine2.mSignalDecomposition.mTrPerfDetails.head())
lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine2.standrdPlots(name="outputs/rnn_my_airline_passengers")
dfapp_in = df.copy()
dfapp_in.tail()
# H = 12
dfapp_out = lEngine2.forecast(dfapp_in, H)
dfapp_out.tail(2 * H)
print("Forecast Columns ", dfapp_out.columns)
lForecastColumnName = b1.mSignalVar + '_Forecast'
Forecast_DF = dfapp_out[[
b1.mTimeVar, b1.mSignalVar, lForecastColumnName,
lForecastColumnName + '_Lower_Bound',
lForecastColumnName + '_Upper_Bound'
]]
print(Forecast_DF.info())
print("Forecasts\n", Forecast_DF.tail(2 * H))
print("\n\n<ModelInfo>")
print(lEngine2.to_json())
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2 * H).to_json(date_format='iso'))
print("</Forecast>\n\n")
