Python importAutoRegressiveMovingAverageForecastingDataset Examples

Example #1

File: AutoRegressiveMovingAverageForecastingPreprocess.py Project: Santosh-Sah/AutoRegressive_Moving_Average_Forecasting

def preprocess():

    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset(
        "DailyTotalFemaleBirths.csv")

    X_train, X_test = splitAutoRegressiveMovingAverageForecastingDataset(
        autoRegressiveMovingAverageForecastingDataset)

    saveTrainingAndTestingDataset(X_train, X_test)

Example #2

File: AutoRegressiveMovingAverageForecastingTrainModel.py Project: Santosh-Sah/AutoRegressive_Moving_Average_Forecasting

def trainAutoRegressiveMovingAverageForecastingModelOnFullDataset():
    
    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset("DailyTotalFemaleBirths.csv")
    
    #training model on the whole dataset
    autoRegressiveMovingAverageForecastingModel = ARMA(autoRegressiveMovingAverageForecastingDataset["Births"], order = (2, 2))
    
    autoRegressiveMovingAverageForecastingModelFitResult = autoRegressiveMovingAverageForecastingModel.fit()
    
    autoRegressiveMovingAverageForecastingModelFitResult.summary()
    
    saveAutoRegressiveMovingAverageForecastingModelForFullDataset(autoRegressiveMovingAverageForecastingModelFitResult)

Example #3

def plotAutoRegressiveMovingAverageForecastingForecastedValues():

    #reading the dataset
    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset(
        "DailyTotalFemaleBirths.csv")

    #reading the forecated values
    autoRegressiveMovingAverageForecastingForecastedValues = readAutoRegressiveMovingAverageForecastingForecastedValues(
    )

    #visualizing the forecated values
    visualizeAutoRegressiveMovingAverageForecastingForecastedValues(
        autoRegressiveMovingAverageForecastingDataset,
        autoRegressiveMovingAverageForecastingForecastedValues)

Example #4

def forecastAutoRegressiveMovingAverageForecastingModel():

    #reading the dataset
    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset(
        "DailyTotalFemaleBirths.csv")

    #reading the model whichis trained on the whole dataset
    autoRegressiveMovingAverageForecastingModel = readAutoRegressiveMovingAverageForecastingModelForFullDataset(
    )

    #forecasting for 11 months
    autoRegressiveMovingAverageForecastingForecastedValues = autoRegressiveMovingAverageForecastingModel.predict(
        len(autoRegressiveMovingAverageForecastingDataset),
        len(autoRegressiveMovingAverageForecastingDataset) + 11,
        typ='levels').rename("ARMA(2,2) Prediction")

    #saving the forecasted values
    saveAutoRegressiveMovingAverageForecastingForecastedValues(
        autoRegressiveMovingAverageForecastingForecastedValues)

Example #5

File: AutoRegressiveMovingAverageForecastingTrainModel.py Project: Santosh-Sah/AutoRegressive_Moving_Average_Forecasting

def determineARMAOrderOfPAndQ():
    
    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset("DailyTotalFemaleBirths.csv")
    
    auto_arima(autoRegressiveMovingAverageForecastingDataset["Births"], seasonal = False, trace = True).summary()

Example #6

File: AutoRegressiveMovingAverageForecastingTrainModel.py Project: Santosh-Sah/AutoRegressive_Moving_Average_Forecasting

def testIsDatasetStationary():
    
    autoRegressiveMovingAverageForecastingDataset = importAutoRegressiveMovingAverageForecastingDataset("DailyTotalFemaleBirths.csv")
    
    agumentedDickeyFullerTest(autoRegressiveMovingAverageForecastingDataset["Births"])