automate.py

   '''Helper Funciton to check if seasonality is present'''
   def seasonality_test(self, series):
        self.__seasonal__ = False
        idx = np.arange(len(series.index)) % 12
        H_statistic, p_value = kruskal(series, idx)
        if p_value <= 0.05:
            self.__seasonal__ = True
        return seasonal
    
     '''Helper Function to check if trend is present'''
    def mkTest(series, seasonal):
        
        if seasonal == False:
            data_mk = mk.original_test(series)
            trend = data_mk[0]
        else:
            data_mk_seasonal_test = mk.seasonal_test(series, period= 12)
            trend = data_mk_seasonal_test[0]
        
        if trend == 'decreasing' or trend == 'increasing':
            self.__trend__ = 'present' 
            trend = 'present'
            return trend
        self.__trend__ = trend
        return trend    
      
    
  def holtWinters_TES(self, train, test, trend, seasonal):
        
        if: trend == 'present' and seasonal == True:
            tes_add = ExponentialSmoothing(train, trend = 'add', seasonal = 'add', seasonal_periods= 12).fit().fittedvalues
            tes_add_pred = tes_add.forecast(len(test))
            rmse_tes_add = rootMeanSquaredError(test, tes_add_pred)
            
            tes_mul = ExponentialSmoothing(train, trend = 'mul', seasonal = 'mul', seasonal_periods= 12).fit().fittedvalues
            tes_mul_pred = tes_mul.forecast(len(test))
            rmse_tes_mul = rootMeanSquaredError(test, tes_mul_pred)
            
            if rmse_tes_add < rmse_tes_mul:
                if rmse_tes_add < self.rmse:
                    self.rmse = rmse_tes_add
                    self.__model__ = 'holtWinters_TES_add'
                    self.__model_type__ = 'add'
            else:
                if rmse_tes_mul < self.rmse:
                    self.rmse = rmse_tes_mul
                    self.__model__ = 'holtWinters_TES_mul'
                    self.__model_type__ = 'mul'