Calculating MAPE and MAE

You previously calculated the forecast for the metropolitan region total sales and stored it in the object forecast_MET_t. You also have your validation data set stored in the object MET_t_valid that covers the same first 22 weeks of 2017. Let's see how good your forecast is!

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Forecasting Product Demand in R

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Convert the mean of your forecast (forecast_MET_t$mean) and validation data set (MET_t_valid) to numeric values and save them as for_MET_t and v_MET_t respectively.
Calculate the MAE of your forecast. Remember, this is the average of the absolute difference between the forecast and the true validation values.
Calculate the MAPE of your forecast. This takes the same difference as the MAE, but divides it by the true validation values.
Print both the MAE and MAPE.

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# Convert to numeric for ease
for_MET_t <- as.numeric(___)
v_MET_t <- as.numeric(___)

# Calculate the MAE
MAE <- mean(abs(___ - ___))

# Calculate the MAPE
MAPE <- 100*mean(abs((for_MET_t - v_MET_t)/___))

# Print to see how good your forecast is!
print(MAE)
print(MAPE)

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Diese Übung ist Teil des Kurses

Forecasting Product Demand in R

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When it comes to forecasting, time series modeling is a great place to start! You need to forecast out the future values of sales demand and a good baseline approach would be ARIMA models. In this chapter you'll learn how to quickly implement ARIMA models and get good initial forecasts for future product demand.

Exercise 1: Loading data into xts object Exercise 2: Importing data Exercise 3: Plotting / visualizing data Exercise 4: ARIMA Time Series 101 Exercise 5: auto.arima() function Exercise 6: Interpret auto.arima Exercise 7: Forecasting and Evaluating Exercise 8: Forecast function Exercise 9: Calculating MAPE and MAE

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