Regressione per effetti di festività/promozioni

Ora che hai creato la variabile indicatrice per Capodanno, vediamo se è significativamente diversa dal consueto andamento delle vendite usando una regressione.

Il tuo data.frame con il log delle vendite e il log dei prezzi è salvato nel tuo workspace come MET_hi_train. La variabile di Capodanno è salvata come newyear.

Questo esercizio fa parte del corso

Previsione della domanda di prodotto in R

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Istruzioni dell'esercizio

Crea un nuovo insieme di dati, MET_hi_train_2, combinando MET_hi_train e la tua variabile di Capodanno come vettore.
Costruisci un modello di regressione per il prodotto di fascia alta nella regione metropolitana. Il modello deve prevedere il log delle vendite (log_sales) usando il log del prezzo (log_price) e la variabile di Capodanno (newyear), e deve usare il nuovo dataset MET_hi_train_2 che hai appena creato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create MET_hi_train_2 by adding newyear
MET_hi_train_2 <- data.frame(MET_hi_train, as.vector(___))
colnames(MET_hi_train_2)[3] <- "newyear"

# Build regressions for the product
model_MET_hi_full <- lm(___ ~ ___ + ___, data = ___)

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 Exercise 7: Regressione per effetti di festività/promozioni

Esercizio in corso

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!