Crea una previsione di serie storica a livello regionale

A volte non hai il tempo di creare previsioni per ogni singolo prodotto, quindi adottiamo un approccio top-down al forecasting gerarchico. Questa volta lavoriamo sulla regione metropolitana al contrario!

Nel tuo workspace trovi già caricati questi oggetti: MET_total per le vendite regionali totali, dates_valid e MET_t_v per l’insieme di dati di validazione.

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Previsione della domanda di prodotto in R

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Istruzioni dell'esercizio

Costruisci un modello di serie storica per tutte le vendite della regione metropolitana (MET_total).
Esegui una previsione di 22 valori nel 2017 a partire da questo modello.
Trasforma questa previsione in un oggetto xts.
Calcola la MAPE.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Build a regional time series model using auto.arima
MET_t_model_arima <- ___(___)

# Calculate a 2017 forecast for 22 periods from the above model
for_MET_t <- ___(___, h = ___)

# Make an xts object from your forecast
for_MET_t_xts <- ___(___$mean, order.by = ___)

# Calculate the MAPE
MAPE <- mape(___, ___)
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: 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 Exercise 10: Visualizing Forecast Exercise 11: Confidence Intervals for 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: Previsioni gerarchiche bottom-up Exercise 2: Crea un forecast di serie temporale per il nuovo prodotto Exercise 3: Crea una previsione di regressione per un nuovo prodotto Exercise 4: Ensemble di previsione per il nuovo prodotto Exercise 5: Calcola il forecast bottom-up per l'intera regione Exercise 6: Previsioni gerarchiche Top-Down Exercise 7: Crea una previsione di serie storica a livello regionale

Esercizio in corso

Exercise 8: Uso delle proporzioni storiche medie Exercise 9: Uso della proporzione delle medie storiche Exercise 10: Previsioni gerarchiche middle-out Exercise 11: Crea una previsione di serie temporale per la nuova regione Exercise 12: Previsione top-down per una nuova regione Exercise 13: Previsione bottom-up per l’intero stato Exercise 14: Congratulazioni!