Bangun peramalan deret waktu untuk produk baru

Sebelum kita dapat menghitung peramalan bottom-up untuk wilayah metropolitan, kita perlu memiliki peramalan untuk beberapa produk! Pertama, mari bangun peramalan deret waktu untuk produk spesial di wilayah metropolitan. Permintaan produk disimpan sebagai MET_sp di ruang kerja Anda, juga dates_valid, serta data validasi Anda MET_sp_v.

Anda sudah cukup sering menulis fungsi MAPE sampai pada titik ini. Kini sebuah fungsi mape() telah dituliskan untuk Anda gunakan dengan dua masukan: yang pertama adalah hasil peramalan dan yang kedua adalah himpunan validasi.

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

Petunjuk latihan

Gunakan fungsi auto.arima() untuk membangun model deret waktu bagi produk spesial MET_sp.
Ramalkan model ini selama 22 periode waktu hingga tahun 2017.
Ubah hasil peramalan ini menjadi objek xts. Anda tetap dapat menggunakan objek dates_valid untuk opsi order.by =.
Hitung MAPE untuk peramalan ini dengan fungsi mape() yang baru.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Build a time series model 
MET_sp_model_arima <- ___(___)

# Forecast the time series model you just built for 22 periods
for_MET_sp <- ___(___, h = ___)

# Create an xts object on the forecast object you just created
for_MET_sp_xts <- ___(___$mean, order.by = ___)

# Calculate the MAPE on your forecast with the validation data
MAPE <- mape(___, ___)
print(MAPE)

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

Latihan Saat Ini

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 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!