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| import numpy as np | |
| import pandas as pd | |
| from datetime import datetime | |
| import pmdarima as pm | |
| from pmdarima import auto_arima | |
| def train_test(dataframe, n): | |
| training_y = dataframe.iloc[:-n,0] | |
| test_y = dataframe.iloc[-n:,0] | |
| test_y_series = pd.Series(test_y, index=dataframe.iloc[-n:, 0].index) | |
| training_X = dataframe.iloc[:-n,1:] | |
| test_X = dataframe.iloc[-n:,1:] | |
| future_X = dataframe.iloc[0:,1:] | |
| return (training_y, test_y, test_y_series, training_X, test_X, future_X) | |
| def model_fitting(dataframe, Exo): | |
| futureModel = pm.auto_arima(dataframe['Sales'], X=Exo, start_p=1, start_q=1, | |
| test='adf',min_p=1,min_q=1, | |
| max_p=3, max_q=3, m=12, | |
| start_P=0, seasonal=True, | |
| d=None, D=1, trace=True, | |
| error_action='ignore', | |
| suppress_warnings=True, | |
| stepwise=True) | |
| model = futureModel | |
| return model | |
| def test_fitting(dataframe, Exo, trainY): | |
| trainTestModel = auto_arima(X = Exo, y = trainY, start_p=1, start_q=1, | |
| test='adf',min_p=1,min_q=1, | |
| max_p=3, max_q=3, m=12, | |
| start_P=0, seasonal=True, | |
| d=None, D=1, trace=True, | |
| error_action='ignore', | |
| suppress_warnings=True, | |
| stepwise=True) | |
| model = trainTestModel | |
| return model | |
| def forecast_accuracy(forecast, actual): | |
| mape = np.mean(np.abs(forecast - actual)/np.abs(actual)).round(4) # MAPE | |
| rmse = (np.mean((forecast - actual)**2)**.5).round(2) # RMSE | |
| corr = np.corrcoef(forecast, actual)[0,1] # corr | |
| mins = np.amin(np.hstack([forecast[:,None], | |
| actual[:,None]]), axis=1) | |
| maxs = np.amax(np.hstack([forecast[:,None], | |
| actual[:,None]]), axis=1) | |
| minmax = 1 - np.mean(mins/maxs) # minmax | |
| return({'mape':mape, 'rmse':rmse, 'corr':corr, 'min-max':minmax}) | |
| def sales_growth(dataframe, fittedValues): | |
| sales_growth = fittedValues.to_frame() | |
| sales_growth = sales_growth.reset_index() | |
| sales_growth.columns = ("Date", "Sales") | |
| sales_growth = sales_growth.set_index('Date') | |
| sales_growth['Sales'] = (sales_growth['Sales']).round(2) | |
| #Calculate and create the column for sales difference and growth | |
| sales_growth['Forecasted Sales First Difference']=(sales_growth['Sales']-sales_growth['Sales'].shift(1)).round(2) | |
| sales_growth['Forecasted Sales Growth']=(((sales_growth['Sales']-sales_growth['Sales'].shift(1))/sales_growth['Sales'].shift(1))*100).round(2) | |
| #Calculate and create the first row for sales difference and growth | |
| sales_growth['Forecasted Sales First Difference'].iloc[0] = (dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2]).round(2) | |
| sales_growth['Forecasted Sales Growth'].iloc[0]=(((dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2])/dataframe['Sales'].iloc[-1])*100).round(2) | |
| return sales_growth |