Pembedaan musiman untuk stasioneritas

Untuk data musiman, pembedaan (differencing) sering dilakukan antara observasi pada musim yang sama di tahun-tahun berturutan, bukan pada periode berturutan. Misalnya, untuk data kuartalan, Anda akan mengambil selisih antara Q1 pada satu tahun dan Q1 pada tahun sebelumnya. Ini disebut pembedaan musiman (seasonal differencing).

Terkadang Anda perlu menerapkan pembedaan musiman dan pembedaan lag-1 pada deret yang sama, sehingga menghitung selisih dari selisih.

Pada latihan ini, Anda akan menggunakan pembedaan dan transformasi secara bersamaan untuk membuat sebuah deret waktu tampak stasioner. Himpunan data yang digunakan adalah h02, yang berisi penjualan obat kortikosteroid bulanan selama 17 tahun di Australia. Data tersebut telah dimuat ke dalam workspace Anda.

Latihan ini adalah bagian dari kursus

Peramalan di R

Petunjuk latihan

Plot datanya untuk mengamati tren dan musimannya.
Ambil log() dari data h02 lalu terapkan pembedaan musiman dengan menggunakan nilai lag yang sesuai pada diff(). Simpan sebagai difflogh02.
Plot data hasil log dan pembedaan tersebut.
Karena difflogh02 masih tampak tidak stasioner, ambil pembedaan lag-1 lagi dengan menerapkan diff() pada dirinya sendiri dan simpan sebagai ddifflogh02. Plot deret hasilnya.
Plot ACF dari deret final ddifflogh02 menggunakan fungsi yang sesuai.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot the data
___

# Take logs and seasonal differences of h02
difflogh02 <- diff(log(___), lag = ___)

# Plot difflogh02
___

# Take another difference and plot
ddifflogh02 <- ___
___

# Plot ACF of ddifflogh02
___

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: 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: Transformasi untuk menstabilkan varians Exercise 2: Transformasi Box-Cox untuk deret waktu Exercise 3: Differencing non-musiman untuk stasioneritas Exercise 4: Pembedaan musiman untuk stasioneritas

Latihan Saat Ini

Exercise 5: Model ARIMA Exercise 6: Model ARIMA otomatis untuk deret waktu non-musiman Exercise 7: Peramalan dengan model ARIMA Exercise 8: Membandingkan auto.arima() dan ets() pada data non-musiman Exercise 9: Model ARIMA Musiman Exercise 10: Model ARIMA otomatis untuk deret waktu musiman Exercise 11: Mengeksplorasi opsi auto.arima()Exercise 12: Membandingkan auto.arima() dan ets() pada data musiman

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!