def cfeMovingAverage(self, k): cfe_moving_average = classPrediction.Prediction(values) leng = len(self.a_list[1]["D(t)"]) cfe = 0 for i in range(cfe_moving_average.movingAverage(k), leng): Ed = self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i] cfe = cfe + Ed print(Ed) print("The CFE for Moving Average is: ", cfe)
def cfeCustomizedExponentialSmoothing(self, start): cfe_exponential_smoothing = classPrediction.Prediction(values) cfe_exponential_smoothing.customizedExponentialSmoothing(start) leng = len(self.a_list[1]["D(t)"]) cfe = 0 for i in range(start, leng): Ed = self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i] cfe = cfe + Ed print(Ed) print("The CFE for Customized Exponential Smoothing is: ", cfe)
def mseNaivePrediction(self): mse_naive_prediction = classPrediction.Prediction(values) mse_naive_prediction.naivePrediction() leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mse = 0 for i in range(1, leng_Dt): Ed = (self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i])**2 mse = mse + Ed print(Ed) mean = mse / (leng_Ft - 1) print("The MSE error for Naive Prediction is: ", mean)
def cfeStationaryMobileMedia(self, k): cfe_stationary_mobile_media = classPrediction.Prediction(values) leng = len(self.a_list[1]["D(t)"]) cfe = 0 for i in range(cfe_stationary_mobile_media.stationaryMobileMedia(k), leng): Ed = self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i] cfe = cfe + Ed print(Ed) print("The CFE for Stationary Mobile Media is: ", cfe)
def cfeNaivePrediction(self): cfe_naive_prediction = classPrediction.Prediction(values) cfe_naive_prediction.naivePrediction() leng = len(self.a_list[1]["D(t)"]) cfe = 0 for i in range(1, leng): Ed = self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i] cfe = cfe + Ed print(Ed) print("The CFE for Naive Prediction is: ", cfe)
def madExponentialSmoothing(self, start): mad_exponential_smoothing = classPrediction.Prediction(values) mad_exponential_smoothing.exponentialSmoothing(start) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mad = 0 for i in range(start, leng_Dt): Ed = abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) print(Ed) mad = mad + Ed mean = mad / (leng_Ft - start) print("The MAD error for Exponential Smoothing is: ", mean)
def madStationaryMobileMedia(self, k): mad_stationary_mobile_media = classPrediction.Prediction(values) mad_stationary_mobile_media.stationaryMobileMedia(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mad = 0 for i in range(k, leng_Dt): Ed = abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) print(Ed) mad = mad + Ed mean = mad / (leng_Ft - k) print("The MAD error for Stationary Mobile Media is: ", mean)
def mseStationaryMobileMedia(self, k): mse_stationery_mobile_media = classPrediction.Prediction(values) mse_stationery_mobile_media.stationaryMobileMedia(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mse = 0 for i in range(k, leng_Dt): Ed = (self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i])**2 print(Ed) mse = mse + Ed mean = mse / (leng_Ft - k) print("The MSE error for Stationary Mobile Media is: ", mean)
def mseMovingAverage(self, k): mse_moving_average = classPrediction.Prediction(values) mse_moving_average.movingAverage(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mse = 0 for i in range(k, leng_Dt): Ed = (self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i])**2 print(Ed) mse = mse + Ed mean = mse / (leng_Ft - k) print("The MSE error for Moving Average is: ", mean)
def mapeMovingAverage(self, k): mape_moving_average = classPrediction.Prediction(values) mape_moving_average.movingAverage(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mape = 0 for i in range(k, leng_Dt): Ed = (abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) / self.a_list[1]["D(t)"][i]) print(Ed) mape = mape + Ed mean = mape / (leng_Ft - k) print("The MAPE error for Moving Average is {} %".format(mean * 100))
def madMovingAverage(self, k): mad_moving_Average = classPrediction.Prediction(values) mad_moving_Average.movingAverage(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mad = 0 for i in range(k, leng_Dt): Ed = abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) print(Ed) mad = mad + Ed mean = mad / (leng_Ft - k) print("The MAD error for Moving Average is: ", mean)
def madNaivePrediction(self): mad_naive_prediction = classPrediction.Prediction(values) mad_naive_prediction.naivePrediction() leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mad = 0 for i in range(1, leng_Dt): Ed = abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) mad = mad + Ed print(Ed) mean = mad / (leng_Ft - 1) print("The MAD error for Naive Prediction is: ", mean)
def mapeExponentialSmoothing(self, start): mape_exponential_smoothing = classPrediction.Prediction(values) mape_exponential_smoothing.exponentialSmoothing(start) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mape = 0 for i in range(start, leng_Dt): Ed = (abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) / self.a_list[1]["D(t)"][i]) print(Ed) mape = mape + Ed mean = mape / (leng_Ft - start) print("The MAPE error for Exponential Smoothing is {} %".format(mean * 100))
def mapeStationaryMobileMedia(self, k): mape_stationary_mobile_media = classPrediction.Prediction(values) mape_stationary_mobile_media.stationaryMobileMedia(k) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mape = 0 for i in range(k, leng_Dt): Ed = (abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) / self.a_list[1]["D(t)"][i]) print(Ed) mape = mape + Ed mean = mape / (leng_Ft - k) print("The MAPE error for Stationary Mobile Media is {} %".format( mean * 100))
def mapeNaivePrediction(self): mape_naive_prediction = classPrediction.Prediction(values) mape_naive_prediction.naivePrediction() leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mape = 0 for i in range(1, leng_Dt): Ed = (abs(self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i]) / self.a_list[1]["D(t)"][i]) print(Ed) mape = mape + Ed mean = mape / (leng_Ft - 1) print("The MAPE error for Naive Prediction is: {} % ".format(mean * 100))
def mseExponentionalSmoothing(self, start): mse_exponential_smoothing = classPrediction.Prediction(values) mse_exponential_smoothing.exponentialSmoothing(start) leng_Dt = len(self.a_list[1]["D(t)"]) leng_Ft = len(self.a_list[2]["F(t)"]) mse = 0 print(self.a_list[1]["D(t)"]) print(self.a_list[2]["F(t)"]) for i in range(start, leng_Dt): Ed = (self.a_list[1]["D(t)"][i] - self.a_list[2]["F(t)"][i])**2 print(Ed) mse = mse + Ed mean = mse / (leng_Ft - start) print("The MSE error for Exponential Smoothing is: ", mean)