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)
