Visualiser les prévisions des résidus

Comme tout au long du cours, nous allons visualiser l’allure de ces prévisions. Votre objet de modèle est for_MET_hi_arima. Rappelez-vous : votre prévision est stockée dans l’élément mean de votre objet de modèle.

Cet exercice fait partie du cours

<cours>Prévoir la demande de produits avec R</cours>

Instructions de l’exercice

Convertissez votre prévision en un objet xts. Utilisez le code de l’objet dates_valid fourni comme index.
Tracez votre prévision.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Convert your forecasts into an xts object
dates_valid <- seq(as.Date("2017-01-01"), length = 22, by = "weeks")
for_MET_hi_arima <- xts(___$___, order.by = ___)

# Plot the forecast
plot(___)

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Prévoir la demande de produits avec R</cours>

IntermédiaireNiveau de compétence

4.7+

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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: Résidus du modèle de régression Exercise 2: Calcul des résidus d’une régression Exercise 3: Visualisation des résidus Exercise 4: Prévoir les résidus Exercise 5: Fonction auto.arima Exercise 6: Prévoir les résidus avec des séries temporelles Exercise 7: Visualiser les prévisions des résidus

Exercice actuel

Exercise 8: Fonctions de transfert et ensemblage Exercise 9: Combiner les résidus de la régression et de la série temporelle Exercise 10: Visualiser la prévision avec fonction de transfert Exercise 11: Calculer la MAPE et la MAE de la fonction de transfert Exercise 12: Prévision ARIMA Exercise 13: Mise en ensemble des prévisions

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!