stl_decomposition.py

def decompose_time_series(series, desired_frequency):
    """
    Perform STL decomposition on the time series.
    Arguments:
        series: Pandas series. Time series sequence that we wish to decompose.
        desired_frequency: Integer. Time frequency of the series. If we want to detect 
        a yearly trend, we'd set the value equal to 365.
    Outputs:
        Plot of time series STL decomposition.
    """
    result = seasonal_decompose(series, model='additive', freq=desired_frequency)
    result.plot()
    plt.show()

##EXECUTE IN MAIN BLOCK

#APPLY S-ESD ALGORITHM TO DETECT ANOMALIES
#Decompose time series on a yearly interval
#Set Date as index for the time series
gasoline_price_df.index=gasoline_price_df['Date']
decompose_time_series(series=gasoline_price_df['Gasoline_Price'], 
                          desired_frequency=365)