ETS vs seasonal naive

Di sini, Anda akan membandingkan peramalan ETS dengan peramalan seasonal naive untuk 20 tahun data cement, yang berisi produksi semen triwulanan, menggunakan validasi silang deret waktu untuk 4 langkah ke depan. Karena ini memerlukan waktu untuk dijalankan, versi singkat dari deret cement akan tersedia di workspace Anda.

Argumen kedua untuk tsCV() harus mengembalikan objek ramalan, jadi Anda memerlukan sebuah fungsi yang memasangkan model dan mengembalikan ramalan. Ingat:

> args(tsCV)
function (y, forecastfunction, h = 1, ...)

Dalam latihan ini Anda akan menggunakan sebuah fungsi peramalan yang sudah ada serta satu fungsi yang telah dibuatkan untuk Anda. Ingat, terkadang metode sederhana bekerja lebih baik daripada metode yang lebih canggih!

Latihan ini adalah bagian dari kursus

Peramalan di R

Petunjuk latihan

Sebuah fungsi untuk mengembalikan ramalan ETS, fets(), telah dituliskan untuk Anda.
Terapkan tsCV() untuk metode ETS dan seasonal naive pada data cement dengan horizon ramalan 4. Gunakan fungsi fets yang baru dibuat dan fungsi snaive yang sudah ada sebagai argumen fungsi peramalan untuk e1 dan e2, masing-masing.
Hitung MSE dari galat 4 langkah yang dihasilkan dan hilangkan nilai yang hilang. Ekspresi untuk menghitung MSE telah disediakan, tetapi argumen opsional keduanya belum diisi (Anda sudah pernah menggunakannya sebelumnya).
Simpan MSE terbaik sebagai bestmse. Anda dapat menyalin seluruh baris kode yang menghasilkan MSE terbaik dari instruksi sebelumnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Function to return ETS forecasts
fets <- function(y, h) {
  forecast(ets(y), h = h)
}

# Apply tsCV() for both methods
e1 <- tsCV(___, ___, h = ___)
e2 <- tsCV(___, ___, h = ___)

# Compute MSE of resulting errors (watch out for missing values)
mean(e1^2, ___)
mean(e2^2, ___)

# Copy the best forecast MSE
bestmse <- ___

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: Forecasts and potential futures Exercise 2: Naive forecasting methods Exercise 3: Fitted values and residuals Exercise 4: Checking time series residuals Exercise 5: Training and test sets Exercise 6: Evaluating forecast accuracy of non-seasonal methods Exercise 7: Evaluating forecast accuracy of seasonal methods Exercise 8: Do I have a good forecasting model?Exercise 9: Time series cross-validation Exercise 10: Using tsCV() for time series cross-validation

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: Peramalan berbobot eksponensial Exercise 2: Pemulusan eksponensial sederhana Exercise 3: SES vs naive Exercise 4: Metode pemulusan eksponensial dengan tren Exercise 5: Metode tren Holt Exercise 6: Metode pemulusan eksponensial dengan tren dan musiman Exercise 7: Holt-Winters dengan data bulanan Exercise 8: Metode Holt-Winters dengan data harian Exercise 9: Model ruang keadaan untuk exponential smoothing Exercise 10: Peramalan otomatis dengan exponential smoothing Exercise 11: ETS vs seasonal naive

Latihan Saat Ini

Exercise 12: Cocokkan model dengan deret waktunya Exercise 13: Kapan ETS gagal?

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!