Crea variabili per effetti di festività/promozioni

Nel precedente esercizio abbiamo notato un accenno di stagionalità, ma verifichiamo se c’è davvero qualcosa. I tuoi capi pensano che i loro prodotti siano più richiesti nelle settimane di Natale, Capodanno e San Valentino. Il reparto marketing segnala anche che negli ultimi 5 anni hanno lanciato promozioni nella settimana precedente la Festa della Mamma. Creiamo una variabile indicatrice binaria per Capodanno!

Questo esercizio fa parte del corso

Previsione della domanda di prodotto in R

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Istruzioni dell'esercizio

Crea un indice di date per le settimane di Capodanno nel tuo insieme di dati di training chiamato n.dates.
Costruisci un oggetto xts chiamato newyear usando l’indice di date n.dates.
Crea una sequenza di 154 date dal 19 gen 2014 (il periodo del tuo training set) chiamata dates_train.
Unisci l’oggetto newyear con le date di training dell’ultimo punto e riempi tutti i valori mancanti con 0.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create date indices for New Year's week
n.dates <- as.Date(c("2014-12-28", "2015-12-27", "2016-12-25"))

# Create xts objects for New Year's
newyear <- as.xts(rep(1, 3), order.by = ___)

# Create sequence of 154 weeks for merging
dates_train <- seq(as.Date("2014-01-19"), length = ___, by = ___)

# Merge training dates into New Year's object
newyear <- ___(newyear, ___, fill = ___)

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: Elasticità del prezzo Exercise 2: Calcolare l'elasticità prezzo Exercise 3: Interpreta i risultati dell’elasticità Exercise 4: Effetti stagionali, festivi e promozionali Exercise 5: Visualizza gli effetti di festività/promozioni Exercise 6: Crea variabili per effetti di festività/promozioni

Esercizio in corso

Exercise 7: Regressione per effetti di festività/promozioni Exercise 8: Effetti significativi di festività o promozioni?Exercise 9: Previsioni con la regressione Exercise 10: Crea variabili predittive future Exercise 11: Prevedi i valori futuri della domanda Exercise 12: Visualizzare le previsioni della regressione

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!