Fungsi auto.arima()

Kita dapat menggunakan fungsi auto.arima untuk membantu kita memilih model awal yang baik secara otomatis. Data penjualan regional Anda yang dijumlahkan untuk semua produk di wilayah metropolitan telah dimuat ke dalam ruang kerja Anda sebagai objek MET_t. Kita akan menggunakan fungsi index untuk membantu dengan tanggal-tanggal ini.

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

Petunjuk latihan

Bagi data menjadi bagian pelatihan dan validasi, dengan validasi berisi seluruh data tahun 2017 Anda. Bagian pelatihan telah disiapkan untuk Anda, sekarang Anda coba buat bagian validasi! Pastikan menggunakan format tanggal YYYY-MM-DD.
Jalankan fungsi auto.arima() pada data pelatihan penjualan regional metropolitan Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Split the data into training and validation
MET_t_train <- MET_t[index(MET_t) < "2017-01-01"]
MET_t_valid <- ___[index(___) >= "___"]

# Use auto.arima() function for metropolitan sales training data
auto.arima(___)

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: Memuat data ke dalam objek xts Exercise 2: Mengimpor data Exercise 3: Memplot / memvisualisasikan data Exercise 4: Pengantar Deret Waktu ARIMA 101 Exercise 5: Fungsi auto.arima()

Latihan Saat Ini

Exercise 6: Menginterpretasikan auto.arima Exercise 7: Peramalan dan Evaluasi Exercise 8: Fungsi forecast Exercise 9: Menghitung MAPE dan MAE Exercise 10: Memvisualisasikan Prakiraan Exercise 11: Interval Kepercayaan untuk Peramalan

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!