Python ForecastEngine.cForecastEngine示例

示例#1

文件： hand-PyAF.py 项目： sada-narayanappa/Notebooks

df2 = df.copy()
df2=df2.rename(columns={"sdttm": "Date"})

columns = '''Date,pefmax,so2,co,o3,no2,temperaturec,windspeedms,precipitationpercent,vaporpressurehpa,
dewpointtemperaturec,airpressurehpa,sealevelpressurehpa,groundtemperaturec,tmax,amax,tmin'''
#amin,pmin,tmaxlesstmin,amaxlessamin,pmaxlesspmin'''
cs = [c.strip() for c in columns.split(',')]

df2=df2[cs]
#df2.dtypes


# In[6]:

import pyaf.ForecastEngine as autof
lEngine = autof.cForecastEngine()

lExogenousData = (df2 , cs[2:6]) 
df3=df2["Date,pefmax".split(',')].copy()


# In[ ]:

lEngine.train(df3, 'Date' , 'pefmax', 12 , lExogenousData);


# In[7]:

import pyaf.ForecastEngine as autof
lEngine_Without_Exogenous = autof.cForecastEngine()
lEngine_Without_Exogenous.train(df3 , 'Date' , 'pefmax', 7);

示例#2

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")

示例#3

文件： test_ozone_long_series_hours.py 项目： olivier2311/pyaf

    return df1;

b1 = tsds.load_ozone()
df = b1.mPastData

NH = 400
df1 = replicate(df, NH);
df1.head()

#df.tail(10)
#df[:-10].tail()
#df[:-10:-1]
#df.describe()


lEngine = autof.cForecastEngine()
lEngine

H = NH // 4;
# lEngine.mOptions.enable_slow_mode();
lEngine.mOptions.mDebug = True;
lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.mParallelMode = False

lEngine.train(df1 , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());

lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution

lEngine.standardPlots("outputs/my_long_ozone_series_");

示例#4

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)

    # get_ipython().magic('matplotlib inline')

    b1 = tsds.load_ozone()
    df = b1.mPastData

    # df.tail(10)
    # df[:-10].tail()
    # df[:-10:-1]
    # df.describe()

    lEngine = autof.cForecastEngine()
    lEngine

    H = b1.mHorizon
    # lEngine.mOptions.enable_slow_mode();
    lEngine.mOptions.mDebugPerformance = True
    lEngine.mOptions.mParallelMode = iParallel
    lEngine.mOptions.set_active_autoregressions(['MLP', 'LSTM'])
    lEngine.train(df, b1.mTimeVar, b1.mSignalVar, H)

    lEngine2 = pickleModel(lEngine)
    lEngine2.getModelInfo()
    print(lEngine2.mSignalDecomposition.mTrPerfDetails.head())

    lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution

    lEngine2.standrdPlots("outputs/my_rnn_ozone")

    dfapp_in = df.copy()
    dfapp_in.tail()

    # H = 12
    dfapp_out = lEngine2.forecast(dfapp_in, H)
    # dfapp_out.to_csv("outputs/rnn_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(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")