def sarima1(input_df, kunag, matnr, n, l, p, d, q):
index = str(kunag) + "-" + str(matnr)
dfw = n_series(df, kunag, matnr)
test1 = test(df, kunag, matnr)
mae = []
order = (p, d, q)
for i in range(0, l):
k = n - i
lst = []
train1, cv1 = train_cv_split(df, kunag, matnr, k, l)
y_hat_avg = cv1.copy()
for j in range(k, l, -1):
train, cv = train_cv_split(df, kunag, matnr, j, l)
dd = np.asarray(train["quantity"])
fit1 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred = fit1.predict(1)
lst.append(pred.iloc[-1])
pd.DataFrame(lst)
y_hat_avg['pred_column'] = lst
rms = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_column))
mae1 = mean_absolute_error(test1.quantity, y_hat_avg.pred_column)
mae.append(mae1)
l = l - 1
return mae
def moving_average1(input_df, kunag, matnr, n, l, roll):
index = str(kunag) + "-" + str(matnr)
dfw = n_series(df, kunag, matnr)
test1 = test(df, kunag, matnr)
mae = []
for i in range(0, l):
k = n - i
lst = []
train1, cv1 = train_cv_split(df, kunag, matnr, k, l)
y_hat_avg = cv1.copy()
for j in range(k, l, -1):
train, cv = train_cv_split(df, kunag, matnr, j, l)
dd = np.asarray(train["quantity"])
y_hat_avg['moving_avg_forecast'] = train['quantity'].rolling(
roll).mean().iloc[-1]
pred = y_hat_avg['moving_avg_forecast']
lst.append(pred.iloc[-1])
pd.DataFrame(lst)
y_hat_avg['pred_column'] = lst
rms = sqrt(mean_squared_error(cv1.quantity, y_hat_avg.pred_column))
mae1 = mean_absolute_error(cv1.quantity, y_hat_avg.pred_column)
mae.append(mae1)
del y_hat_avg['moving_avg_forecast']
l = l - 1
return mae
def ses1(input_df, kunag, matnr, n, l, alpha):
index = str(kunag) + "-" + str(matnr)
dfw = n_series(df, kunag, matnr)
test1 = test(df, kunag, matnr)
mae = []
for i in range(0, l):
k = n - i
lst = []
train1, cv1 = train_cv_split(df, kunag, matnr, k, l)
y_hat_avg = cv1.copy()
for j in range(k, l, -1):
train, cv = train_cv_split(df, kunag, matnr, j, l)
dd = np.asarray(train["quantity"])
fit2 = SimpleExpSmoothing(np.asarray(train['quantity'])).fit(
smoothing_level=alpha, optimized=False)
y_hat_avg['SES'] = fit2.forecast(len(cv1))
pred = y_hat_avg['SES']
lst.append(pred.iloc[-1])
pd.DataFrame(lst)
y_hat_avg['pred_column'] = lst
rms = sqrt(mean_squared_error(cv1.quantity, y_hat_avg.pred_column))
mae1 = mean_absolute_error(cv1.quantity, y_hat_avg.pred_column)
mae.append(mae1)
del y_hat_avg['SES']
l = l - 1
return mae
def main(input_df, kunag, matnr, n, l, roll, alpha, p, d, q):
index = str(kunag) + "-" + str(matnr)
dfw = n_series(df, kunag, matnr)
test1 = test(df, kunag, matnr)
mae = []
order1 = (0, 1, 1)
order2 = (0, 1, 2)
order3 = (1, 1, 1)
lst = []
# print("count of predictions done")
for i in range(0, 16):
k = n - i
lst1 = []
lst2 = []
lst3 = []
lst4 = []
lst5 = []
nlst = []
train1, cv1 = train_cv_split(df, kunag, matnr, k, l)
y_hat_avg = cv1.copy()
for j in range(k, l, -1):
train, cv = train_cv_split(df, kunag, matnr, j, l)
dd = np.asarray(train["quantity"])
#model 1 : MOVING AVERAGE
y_hat_avg['moving_avg_forecast'] = train['quantity'].rolling(
roll).mean().iloc[-1]
pred1 = y_hat_avg['moving_avg_forecast']
lst1.append(pred1.iloc[-1])
#model 2 : SES
fit1 = SimpleExpSmoothing(np.asarray(train['quantity'])).fit(
smoothing_level=alpha, optimized=False)
y_hat_avg['SES'] = fit1.forecast(len(cv1))
pred2 = y_hat_avg['SES']
lst2.append(pred2.iloc[-1])
#model 3 : ARIMA
fit2 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order1,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred3 = fit2.predict(1)
lst3.append(pred3.iloc[-1])
#model 4 : ARIMA
fit3 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order2,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred4 = fit3.predict(1)
lst4.append(pred4.iloc[-1])
#model 5 : ARIMA
fit4 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order3,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred5 = fit4.predict(1)
lst5.append(pred4.iloc[-1])
pd.DataFrame(lst1)
pd.DataFrame(lst2)
pd.DataFrame(lst3)
pd.DataFrame(lst4)
pd.DataFrame(lst5)
y_hat_avg['pred_ma'] = lst1
y_hat_avg['pred_ses'] = lst2
y_hat_avg['pred_arima011'] = lst3
y_hat_avg['pred_arima012'] = lst4
y_hat_avg['pred_arima111'] = lst5
rms1 = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_ma))
mae1 = mean_absolute_error(test1.quantity, y_hat_avg.pred_ma)
rms2 = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_ses))
mae2 = mean_absolute_error(test1.quantity, y_hat_avg.pred_ses)
rms3 = sqrt(mean_squared_error(test1.quantity,
y_hat_avg.pred_arima011))
mae3 = mean_absolute_error(test1.quantity, y_hat_avg.pred_arima011)
rms4 = sqrt(mean_squared_error(test1.quantity,
y_hat_avg.pred_arima012))
mae4 = mean_absolute_error(test1.quantity, y_hat_avg.pred_arima012)
rms5 = sqrt(mean_squared_error(test1.quantity,
y_hat_avg.pred_arima111))
mae5 = mean_absolute_error(test1.quantity, y_hat_avg.pred_arima111)
# print(mae1,mae2,mae3)
nlst = [mae1, mae2, mae3, mae4, mae5]
# print(nlst)
m = min(nlst)
# print(m)
if (mae1 == m):
# print("mae1:",mae1)
y_hat_avg['moving_avg_forecast'] = train['quantity'].rolling(
roll).mean().iloc[-1]
pred = y_hat_avg['moving_avg_forecast']
elif (mae2 == m):
# print("mae2:",mae2)
fit1 = SimpleExpSmoothing(np.asarray(train['quantity'])).fit(
smoothing_level=alpha, optimized=False)
y_hat_avg['SES'] = fit1.forecast(len(cv1))
pred = y_hat_avg['SES']
elif (mae3 == m):
# print("mae3:",mae3)
fit2 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order1,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred = fit2.predict(1)
elif (mae4 == m):
# print("mae4:",mae4)
fit3 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order2,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred = fit3.predict(1)
elif (mae5 == m):
# print("mae5:",mae5)
fit4 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order3,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred = fit4.predict(1)
lst.append(pred.iloc[-1])
pd.DataFrame(lst)
l = l - 1
y_hat_avg['pred_column'] = lst
# plt.figure(figsize=(12,8))
# plt.plot( train.set_index("date")['quantity'], label='Train',marker = '.')
# plt.plot(cv1.set_index("date")['quantity'], label='Test',marker = '.')
# plt.plot(y_hat_avg.set_index("date")['pred_column'], label='MAIN' + "_" + str(order),marker = '.')
# plt.legend(loc='best')
# plt.title("MAIN")
# plt.show()
rms = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_column))
mae = mean_absolute_error(test1.quantity, y_hat_avg.pred_column)
del y_hat_avg["moving_avg_forecast"]
del y_hat_avg["SES"]
# print(y_hat_avg)
return y_hat_avg, mae
def main1(input_df, kunag, matnr, n, l, roll, alpha, p, d, q):
index = str(kunag) + "-" + str(matnr)
dfw = n_series(df, kunag, matnr)
test1 = test(df, kunag, matnr)
mae = []
order = (p, d, q)
lst = []
# print("count of predictions done")
for i in range(0, l):
k = n - i
lst1 = []
lst2 = []
lst3 = []
nlst = []
train1, cv1 = train_cv_split(df, kunag, matnr, k, l)
y_hat_avg = cv1.copy()
for j in range(k, l, -1):
train, cv = train_cv_split(df, kunag, matnr, j, l)
dd = np.asarray(train["quantity"])
y_hat_avg['moving_avg_forecast'] = train['quantity'].rolling(
roll).mean().iloc[-1]
pred1 = y_hat_avg['moving_avg_forecast']
lst1.append(pred1.iloc[-1])
fit2 = SimpleExpSmoothing(np.asarray(train['quantity'])).fit(
smoothing_level=alpha, optimized=False)
y_hat_avg['SES'] = fit2.forecast(len(cv1))
pred2 = y_hat_avg['SES']
lst2.append(pred2.iloc[-1])
fit1 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred3 = fit1.predict(1)
lst3.append(pred3.iloc[-1])
pd.DataFrame(lst1)
pd.DataFrame(lst2)
pd.DataFrame(lst3)
y_hat_avg['pred_ma'] = lst1
y_hat_avg['pred_ses'] = lst2
y_hat_avg['pred_arima'] = lst3
rms1 = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_ma))
mae1 = mean_absolute_error(test1.quantity, y_hat_avg.pred_ma)
rms2 = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_ses))
mae2 = mean_absolute_error(test1.quantity, y_hat_avg.pred_ses)
rms3 = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_arima))
mae3 = mean_absolute_error(test1.quantity, y_hat_avg.pred_arima)
nlst = [mae1, mae2, mae3]
m = min(nlst)
if (mae1 == m):
y_hat_avg['moving_avg_forecast'] = train['quantity'].rolling(
roll).mean().iloc[-1]
pred = y_hat_avg['moving_avg_forecast']
elif (mae2 == m):
fit2 = SimpleExpSmoothing(np.asarray(train['quantity'])).fit(
smoothing_level=alpha, optimized=False)
y_hat_avg['SES'] = fit2.forecast(len(cv1))
pred = y_hat_avg['SES']
elif (mae3 == m):
fit1 = sm.tsa.statespace.SARIMAX(train["quantity"],
order=order,
enforce_stationarity=False,
enforce_invertibility=False,
trend="n").fit()
pred = fit1.predict(1)
lst.append(pred.iloc[-1])
# print(len(lst) )
pd.DataFrame(lst)
l = l - 1
y_hat_avg['pred_column'] = lst
# plt.figure(figsize=(12,8))
# plt.plot( train.set_index("date")['quantity'], label='Train',marker = '.')
# plt.plot(cv1.set_index("date")['quantity'], label='Test',marker = '.')
# plt.plot(y_hat_avg.set_index("date")['pred_main'], label='MAIN' + "_" + str(order),marker = '.')
# plt.legend(loc='best')
# plt.title("MAIN")
# plt.show()
rms = sqrt(mean_squared_error(test1.quantity, y_hat_avg.pred_column))
mae = mean_absolute_error(test1.quantity, y_hat_avg.pred_column)
del y_hat_avg["moving_avg_forecast"]
del y_hat_avg["SES"]
# print(y_hat_avg)
print("mae :", mae)
return y_hat_avg, mae