Bölgesel düzeyde zaman serisi tahmini oluştur

Bazen her ürün için tahmin oluşturacak zamanın olmaz; bu yüzden hiyerarşik tahminlemede üstten aşağı (top-down) yaklaşımı kullanırız. Bu kez metropol bölgesinde tersinden çalışalım!

Çalışma alanında önceden yüklenmiş şu nesneler var: toplam bölgesel satışlar için MET_total, dates_valid ve doğrulama veri kümesi için MET_t_v.

Bu egzersiz

R ile Ürün Talebi Tahmini

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Metropol bölgesindeki tüm satışlar (MET_total) için bir zaman serisi modeli kur.
Bu modelden 2017 yılına doğru 22 değer tahmin et.
Bu tahmini bir xts nesnesine dönüştür.
MAPE'yi hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Build a regional time series model using auto.arima
MET_t_model_arima <- ___(___)

# Calculate a 2017 forecast for 22 periods from the above model
for_MET_t <- ___(___, h = ___)

# Make an xts object from your forecast
for_MET_t_xts <- ___(___$mean, order.by = ___)

# Calculate the MAPE
MAPE <- mape(___, ___)
print(MAPE)

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: 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: Alttan Üste Hiyerarşik Tahminleme Exercise 2: Yeni ürün için zaman serisi tahmini oluştur Exercise 3: Yeni ürün için regresyon tahmini oluştur Exercise 4: Yeni ürün için ensemble tahmin Exercise 5: Tüm bölge için bottom-up tahmin hesapla Exercise 6: Yukarıdan Aşağıya Hiyerarşik Tahmin Exercise 7: Bölgesel düzeyde zaman serisi tahmini oluştur

Geçerli Egzersiz

Exercise 8: Ortalama geçmiş oranları kullanma Exercise 9: Tarihsel ortalamaların oranını kullanma Exercise 10: Orta-Çıkışlı Hiyerarşik Tahmin Exercise 11: Yeni bölge için zaman serisi tahmini oluştur Exercise 12: Yeni bölge için top-down tahmin Exercise 13: Tüm eyalet için bottom-up tahmin Exercise 14: Tebrikler!