Menggunakan proporsi dari rata-rata historis

Sekarang mari kita coba melakukan hal yang sama persis, tetapi dengan teknik proporsi dari rata-rata historis. Peramalan Anda disimpan sebagai for_MET_t_xts, objek penjualan Anda adalah MET_hi, MET_lo, MET_sp, dan MET_total, serta Anda memiliki tiga himpunan data validasi bernama MET_hi_v, MET_lo_v, dan MET_sp_v.

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate the average historical proportions
prop_hi_2 <- mean(___)/mean(MET_total)
prop_lo_2 <- mean(___)/___(___)
prop_sp_2 <- ___(___)/___(___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Peramalan Hierarkis Bottom-Up Exercise 2: Bangun peramalan deret waktu untuk produk baru Exercise 3: Bangun peramalan regresi untuk produk baru Exercise 4: Ensemble prakiraan untuk produk baru Exercise 5: Hitung peramalan bottom-up untuk seluruh wilayah Exercise 6: Peramalan Hierarkis Top-Down Exercise 7: Bangun peramalan deret waktu pada tingkat regional Exercise 8: Menggunakan proporsi historis rata-rata Exercise 9: Menggunakan proporsi dari rata-rata historis

Latihan Saat Ini

Exercise 10: Peramalan Hierarkis Middle-Out Exercise 11: Bangun peramalan deret waktu untuk wilayah baru Exercise 12: Peramalan top-down untuk wilayah baru Exercise 13: Prediksi bottom-up untuk seluruh negara bagian Exercise 14: Selamat!