Prevedere i residui con le serie temporali

Ora che hai un modello per questi residui, dobbiamo prevederli. Il tuo modello è salvato come MET_hi_arima.

Questo esercizio fa parte del corso

Previsione della domanda di prodotto in R

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Istruzioni dell'esercizio

Prevedi il tuo modello dei residui.
La previsione deve coprire 22 settimane.
Stampa le prime 10 osservazioni di questa previsione usando la funzione head.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Forecast 22 weeks with your model: for_MET_hi_arima
for_MET_hi_arima <- ___(___, h = ___)

# Print first 10 observations
head(___, n = ___)

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: Residui del modello di regressione Exercise 2: Calcolo dei residui dalla regressione Exercise 3: Visualizzare i residui Exercise 4: Prevedere i residui Exercise 5: Funzione auto.arima Exercise 6: Prevedere i residui con le serie temporali

Esercizio in corso

Exercise 7: Visualizzare le previsioni dei residui Exercise 8: Funzioni di trasferimento ed ensembling Exercise 9: Combinare i residui da regressione e serie storiche Exercise 10: Visualizzare il forecast con funzione di trasferimento Exercise 11: Calcolare MAPE e MAE della funzione di trasferimento Exercise 12: Previsioni ARIMA Exercise 13: Ensembling delle previsioni

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!