Mengevaluasi akurasi peramalan metode musiman

Seperti yang Anda pelajari di bab pertama, fungsi window() menentukan start dan end dari suatu deret waktu menggunakan waktu yang relevan alih-alih nilai indeks. Keduanya dapat diformat sebagai vektor seperti c(year, period) yang juga sebelumnya Anda gunakan sebagai argumen untuk ts(). Sekali lagi, period di sini mengacu pada quarter.

Di sini, Anda akan menggunakan jumlah pengunjung triwulanan Melbourne (visnights[, "VICMetro"]) untuk membuat tiga set pelatihan yang berbeda, masing-masing menghilangkan 1, 2, dan 3 tahun terakhir. Periksa data visnights yang sudah dimuat di konsol Anda sebelum memulai latihan; ini akan membantu Anda menentukan nilai yang benar untuk kata kunci h (yang menentukan jumlah nilai yang ingin Anda ramalkan) dalam metode peramalan Anda.

Kemudian untuk setiap set pelatihan, hitung data satu tahun berikutnya, dan akhirnya bandingkan mean absolute percentage error (MAPE) dari peramalan menggunakan accuracy(). Mengapa menurut Anda MAPE sangat bervariasi?

Latihan ini adalah bagian dari kursus

Peramalan di R

Petunjuk latihan

Gunakan window() untuk membuat tiga set pelatihan dari visnights[,"VICMetro"], dengan menghilangkan 1, 2, dan 3 tahun terakhir; beri nama masing-masing train1, train2, dan train3. Atur kata kunci end sesuai kebutuhan.
Hitung peramalan untuk satu tahun untuk tiap set pelatihan menggunakan metode snaive(). Beri nama masing-masing fc1, fc2, dan fc3.
Mengikuti struktur kode contoh, bandingkan MAPE dari ketiga set peramalan menggunakan fungsi accuracy() dengan himpunan uji Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create three training series omitting the last 1, 2, and 3 years
train1 <- window(visnights[, "VICMetro"], end = c(2015, 4))
train2 <- ___
train3 <- ___

# Produce forecasts using snaive()
fc1 <- snaive(___, h = ___)
fc2 <- ___
fc3 <- ___

# Use accuracy() to compare the MAPE of each series
accuracy(fc1, visnights[, "VICMetro"])["Test set", "MAPE"]
___
___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Peramalan di R

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

The first thing to do in any data analysis task is to plot the data. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. The features that are seen in plots of the data must then be incorporated, as far as possible, into the forecasting methods to be used.

Exercise 1: Welcome to Forecasting Using R Exercise 2: Creating time series objects in R Exercise 3: Time series plots Exercise 4: Seasonal plots Exercise 5: Trends, seasonality, and cyclicity Exercise 6: Autocorrelation of non-seasonal time series Exercise 7: Autocorrelation of seasonal and cyclic time series Exercise 8: Match the ACF to the time series Exercise 9: White noise Exercise 10: Stock prices and white noise

In this chapter, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting method has adequately utilized the available information, and methods for measuring forecast accuracy. Each of the tools discussed in this chapter will be used repeatedly in subsequent chapters as you develop and explore a range of forecasting methods.

Exercise 1: Peramalan dan kemungkinan masa depan Exercise 2: Metode peramalan naif Exercise 3: Nilai terpasang dan residu Exercise 4: Memeriksa residual deret waktu Exercise 5: Himpunan pelatihan dan pengujian Exercise 6: Mengevaluasi akurasi ramalan untuk metode non-musiman Exercise 7: Mengevaluasi akurasi peramalan metode musiman

Latihan Saat Ini

Exercise 8: Apakah saya memiliki model peramalan yang baik?Exercise 9: Cross-validation deret waktu Exercise 10: Menggunakan tsCV() untuk cross-validation deret waktu

Forecasts produced using exponential smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. In other words, the more recent the observation, the higher the associated weight. This framework generates reliable forecasts quickly and for a wide range of time series, which is a great advantage and of major importance to applications in business.

Exercise 1: Exponentially weighted forecasts Exercise 2: Simple exponential smoothing Exercise 3: SES vs naive Exercise 4: Exponential smoothing methods with trend Exercise 5: Holt's trend methods Exercise 6: Exponential smoothing methods with trend and seasonality Exercise 7: Holt-Winters with monthly data Exercise 8: Holt-Winters method with daily data Exercise 9: State space models for exponential smoothing Exercise 10: Automatic forecasting with exponential smoothing Exercise 11: ETS vs seasonal naive Exercise 12: Match the models to the time series Exercise 13: When does ETS fail?

ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.

Exercise 1: Transformations for variance stabilization Exercise 2: Box-Cox transformations for time series Exercise 3: Non-seasonal differencing for stationarity Exercise 4: Seasonal differencing for stationarity Exercise 5: ARIMA models Exercise 6: Automatic ARIMA models for non-seasonal time series Exercise 7: Forecasting with ARIMA models Exercise 8: Comparing auto.arima() and ets() on non-seasonal data Exercise 9: Seasonal ARIMA models Exercise 10: Automatic ARIMA models for seasonal time series Exercise 11: Exploring auto.arima() options Exercise 12: Comparing auto.arima() and ets() on seasonal data

The time series models in the previous chapters work well for many time series, but they are often not good for weekly or hourly data, and they do not allow for the inclusion of other information such as the effects of holidays, competitor activity, changes in the law, etc. In this chapter, you will look at some methods that handle more complicated seasonality, and you consider how to extend ARIMA models in order to allow other information to be included in the them.

Exercise 1: Dynamic regression Exercise 2: Forecasting sales allowing for advertising expenditure Exercise 3: Forecasting electricity demand Exercise 4: Dynamic harmonic regression Exercise 5: Forecasting weekly data Exercise 6: Harmonic regression for multiple seasonality Exercise 7: Forecasting call bookings Exercise 8: TBATS models Exercise 9: TBATS models for electricity demand Exercise 10: Your future in forecasting!