Calcolare MAPE e MAE

In precedenza hai calcolato la previsione delle vendite totali della regione metropolitana e l’hai salvata nell’oggetto forecast_MET_t. Hai anche il tuo set di validazione salvato nell’oggetto MET_t_valid, che copre le stesse prime 22 settimane del 2017. Vediamo quanto è buona la tua previsione!

Questo esercizio fa parte del corso

Previsione della domanda di prodotto in R

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Istruzioni dell'esercizio

Converti in valori numerici la media della tua previsione (forecast_MET_t$mean) e il set di validazione (MET_t_valid) e salvale rispettivamente come for_MET_t e v_MET_t.
Calcola il MAE della tua previsione. Ricorda: è la media della differenza assoluta tra la previsione e i veri valori di validazione.
Calcola il MAPE della tua previsione. Usa la stessa differenza del MAE, ma dividila per i veri valori di validazione.
Stampa sia il MAE sia il MAPE.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Previsione della domanda di prodotto 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: Caricare i dati in un oggetto xts Exercise 2: Importazione dei dati Exercise 3: Rappresentare/visualizzare i dati Exercise 4: Serie temporali ARIMA: le basi Exercise 5: Funzione auto.arima()Exercise 6: Interpreta auto.arima Exercise 7: Previsioni e valutazione Exercise 8: Funzione forecast Exercise 9: Calcolare MAPE e MAE

Esercizio in corso

Exercise 10: Visualizzare la previsione Exercise 11: Intervalli di confidenza per il forecast

Economic theory has a lot to say about predicting values of demand. Obviously, external factors like price, seasonality, and timing of promotions will drive some aspects of product demand. In this chapter you'll learn about the basics around price elasticity models and how to incorporate seasonality and promotion timing factors into our product demand forecasts.

Exercise 1: Price elasticity Exercise 2: Calculating price elasticity Exercise 3: Interpret results from elasticity Exercise 4: Seasonal / holiday / promotional effects Exercise 5: Visualize holiday / promotion effects Exercise 6: Create holiday / promotional effect variables Exercise 7: Regression for holiday / promotional effects Exercise 8: Significant holiday or promotional effects?Exercise 9: Forecasting with regression Exercise 10: Create future predictor variables Exercise 11: Forecast future values of demand Exercise 12: Visualizing forecasts of regression

Time series models and pricing regressions don't have to be thought of as separate approaches to product demand forecasting. They can be combined! In this chapter you'll learn about two ways of "combining" the information gained in both modeling approaches - transfer functions and forecast ensembling.

Exercise 1: Residuals from regression model Exercise 2: Calculating residuals from regression Exercise 3: Visualizing residuals Exercise 4: Forecasting residuals Exercise 5: auto.arima function Exercise 6: Forecasting residuals with time series Exercise 7: Visualizing residual forecasts Exercise 8: Transfer functions & ensembling Exercise 9: Combining residuals from regression & time series Exercise 10: Visualizing transfer function forecast Exercise 11: Calculating transfer function MAPE and MAE Exercise 12: ARIMA Forecasting Exercise 13: Ensembling of Forecasts

Everything up until this point deals with making individual models for forecasting product demand. However, we haven't taken advantage of the fact that all of these products form a product hierarchy of sales. Products make up regions and regions make up states. How can we ensure that our forecasts reconcile correctly up and down the hierarchy? In this chapter you'll learn about hierarchical forecasting and how to use it to your advantage in forecasting product demand.

Exercise 1: Bottom-Up Hierarchical Forecasting Exercise 2: Build time series forecast for new product Exercise 3: Build regression forecast for new product Exercise 4: Ensemble forecast for new product Exercise 5: Calculate bottom-up forecast for whole region Exercise 6: Top-Down Hierarchical Forecasting Exercise 7: Build time series forecast at regional level Exercise 8: Using average historical proportions Exercise 9: Using proportion of historical averages Exercise 10: Middle-Out Hierarchical Forecasting Exercise 11: Build time series forecast for new region Exercise 12: Top-down forecast for new region Exercise 13: Bottom-up forecast for whole state Exercise 14: Congratulations!