auto.arima fonksiyonu

Hadi, modelden gelen artık değerleri zaman serisiyle tahmin etmeyi deneyelim! Artık değerlerin MET_hi_full_res içinde saklanıyor.

Bu egzersiz

R ile Ürün Talebi Tahmini

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Artık değerler üzerinde bir model kurmak için auto.arima fonksiyonunu kullan.
Model nesnesini MET_hi_arima olarak kaydet.
Modelin özetine göz at.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Build an ARIMA model on the residuals: MET_hi_arima
MET_hi_arima <- ___(___)

# Look at a summary of the model
___(___)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Ürün Talebi Tahmini

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Regresyon modelinden artıklar Exercise 2: Regresyondan artıkların hesaplanması Exercise 3: Artık değerleri görselleştirme Exercise 4: Artık değerlerin tahmin edilmesi Exercise 5: auto.arima fonksiyonu

Geçerli Egzersiz

Exercise 6: Zaman serisiyle artıkların tahmini Exercise 7: Artık tahminlerini görselleştirme Exercise 8: Transfer fonksiyonları ve topluluk (ensembling)Exercise 9: Regresyon ve zaman serisinden artık değerleri birleştirme Exercise 10: Transfer fonksiyonu tahminini görselleştirme Exercise 11: Transfer fonksiyonu MAPE ve MAE hesaplama Exercise 12: ARIMA Tahminleme Exercise 13: Tahminlerin Ensemblenmesi

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!