Buat variabel efek hari libur / promosi

Kita melihat indikasi musiman pada latihan sebelumnya, tetapi mari kita uji untuk memastikan memang ada. Atasan Anda berpikir bahwa produk mereka akan lebih diminati pada pekan menjelang Natal, Tahun Baru, dan Hari Valentine. Departemen pemasaran juga menyebutkan bahwa mereka telah menjalankan promosi pada pekan sebelum Hari Ibu selama 5 tahun terakhir. Mari kita buat sebuah variabel indikator biner untuk Tahun Baru!

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

Petunjuk latihan

Buat indeks tanggal untuk pekan-pekan Tahun Baru pada himpunan data pelatihan Anda bernama n.dates.
Bangun objek xts bernama newyear menggunakan indeks tanggal n.dates.
Buat urutan 154 tanggal mulai 19 Jan 2014 (rentang waktu data pelatihan Anda) bernama dates_train.
Gabungkan objek newyear dengan tanggal pelatihan dari butir terakhir dan isi semua nilai yang hilang dengan 0.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create date indices for New Year's week
n.dates <- as.Date(c("2014-12-28", "2015-12-27", "2016-12-25"))

# Create xts objects for New Year's
newyear <- as.xts(rep(1, 3), order.by = ___)

# Create sequence of 154 weeks for merging
dates_train <- seq(as.Date("2014-01-19"), length = ___, by = ___)

# Merge training dates into New Year's object
newyear <- ___(newyear, ___, fill = ___)

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: Elastisitas harga Exercise 2: Menghitung elastisitas harga Exercise 3: Menginterpretasikan hasil elastisitas Exercise 4: Dampak musiman/liburan/promosi Exercise 5: Visualisasikan efek hari libur/promosi Exercise 6: Buat variabel efek hari libur / promosi

Latihan Saat Ini

Exercise 7: Regresi untuk efek hari libur/promosi Exercise 8: Apakah ada efek hari raya atau promosi yang signifikan?Exercise 9: Peramalan dengan regresi Exercise 10: Buat variabel prediktor untuk periode mendatang Exercise 11: Meramalkan nilai permintaan di masa depan Exercise 12: Memvisualisasikan peramalan dengan regresi

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!