Fiyat esnekliğini hesaplama

Artık fiyat esnekliklerini bildiğine göre, metropol bölgesindeki üst segment ürün için fiyatlar ne kadar esnek, görelim! Eğitim ve doğrulama veri setleri senin için oluşturuldu ve bev_xts_train ve bev_xts_valid nesnelerinde saklanıyor.

Üst segment ürünün satışları çalışma alanına MET_hi olarak zaten yüklendi. Önce bev_xts_train nesnesinden fiyatları çıkarman gerekiyor. bev_xts_train nesnesindeki fiyat sütununun adı MET.hi.p.

Bu egzersiz

R ile Ürün Talebi Tahmini

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Üst segment ürünün (MET.hi.p) fiyatlarının logaritmasını bir vektör olarak kaydet.
Satışların logaritması ve fiyatların logaritmasını bir veri çerçevesine yükle.
Satışların logaritmasını, fiyatların logaritmasıyla tahmin eden bir regresyon modeli kur.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Save the prices of each product
l_MET_hi_p <- as.vector(___(bev_xts_train[,"___"]))

# Save as a data frame
MET_hi_train <- data.frame(as.vector(log(MET_hi)), l_MET_hi_p)
colnames(MET_hi_train) <- c("log_sales", "log_price")

# Calculate the regression
model_MET_hi <- lm(___ ~ ___, data = ___)

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: Fiyat esnekliği Exercise 2: Fiyat esnekliğini hesaplama

Geçerli Egzersiz

Exercise 3: Esneklik sonuçlarını yorumla Exercise 4: Mevsimsel / tatil / promosyon etkileri Exercise 5: Tatil / promosyon etkilerini görselleştir Exercise 6: Resmi tatil / promosyon etki değişkenleri oluştur Exercise 7: Tatiller / promosyon etkileri için regresyon Exercise 8: Anlamlı tatil veya promosyon etkileri?Exercise 9: Regresyon ile tahmin Exercise 10: Geleceğe yönelik yordayıcı değişkenler oluştur Exercise 11: Gelecek talep değerlerini tahmin et Exercise 12: Regresyon tahminlerini görselleştirme

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!