Betrouwbaarheidsintervallen voor de voorspelling

Je forecast-object forecast_MET_t bevat niet alleen de voorspelling, maar ook foutmarges, de zogeheten betrouwbaarheidsintervallen. Deze intervallen geven aan hoeveel speling er in onze voorspellingen zit, omdat geen enkele voorspelling perfect is.

De voorspelling is opgeslagen als het object forecast_MET_t$mean. De bovengrens en ondergrens van dit interval zijn op vergelijkbare wijze opgeslagen. De tweede kolom van het bovenste betrouwbaarheidsinterval is het 95%-betrouwbaarheidsinterval en is toegankelijk via forecast_MET_t$upper[,2]. Voor de ondergrens vervang je upper door lower.

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Vraag naar producten voorspellen in R

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Oefeninstructies

Zet zowel de bovenste als onderste 95%-grens van het betrouwbaarheidsinterval om naar xts-objecten upper en lower. Je datumindex staat in for_dates.
Voeg deze toe aan de plot van de voorspelling en de validatieset.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Plot the validation data set
plot(MET_t_valid, main = 'Forecast Comparison', ylim = c(4000, 8500))

# Overlay the forecast of 2017
lines(for_MET_t_xts, col = "blue")

# Convert the limits to xts objects
lower <- xts(forecast_MET_t$___[,2], order.by = ___)
upper <- xts(___$___[,___], order.by = for_dates)

# Adding confidence intervals of forecast to plot
lines(lower, col = "blue", lty = "dashed")
lines(___, col = "blue", lty = "dashed")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Vraag naar producten voorspellen in R

SkillTag.level.intermediateSkillTag.label

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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: Data laden in een xts-object Exercise 2: Gegevens importeren Exercise 3: Data plotten/visualiseren Exercise 4: ARIMA-tijdreeks 101 Exercise 5: auto.arima()-functie Exercise 6: auto.arima interpreteren Exercise 7: Voorspellen en Evalueren Exercise 8: Forecast-functie Exercise 9: MAPE en MAE berekenen Exercise 10: Forecast visualiseren Exercise 11: Betrouwbaarheidsintervallen voor de voorspelling

Huidige oefening

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: 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!