Auto Arima

Predict arima model

#Split train and test
train = Wal_sales.iloc[:106,1].values
test = Wal_sales.iloc[106:,1].values
# Train the model
stepwise_model.fit(train)

# Predict test set
pred = stepwise_model.predict(n_periods=37)

# Reframe the data
test_pred = Wal_sales.iloc[106:,:2]
test_pred["Predict_sales"] = np.array(pred,dtype="float")

# Visualize the prediction
plt.figure(figsize=(12,8))
plt.plot( 'Date', 'Weekly_Sales', data=Wal_sales, markersize=12, color='olive', linewidth=3)
plt.plot( 'Date', 'Predict_sales', data=test_pred, marker='', color='blue', linewidth=3)
plt.title("Predicted sales vs Actual sales")
plt.legend()

print("MAPE score: ", mean_absolute_percentage_error(test, pred))