Peramalan data mingguan

Untuk data mingguan, sulit menangani musiman menggunakan model ETS atau ARIMA karena panjang musimannya terlalu besar (sekitar 52). Sebagai gantinya, Anda dapat menggunakan regresi harmonik yang memanfaatkan fungsi sinus dan cosinus untuk memodelkan pola musiman.

Fungsi fourier() memudahkan pembuatan harmonik yang diperlukan. Semakin tinggi orde (\(K\)), semakin "bergelombang" pola musimannya. Dengan \(K=1\), polanya berupa kurva sinus sederhana. Anda dapat memilih nilai \(K\) dengan meminimalkan nilai AICc. Seperti yang Anda lihat di video, fourier() menerima deret waktu yang wajib, jumlah suku Fourier yang wajib untuk dibuat, dan jumlah baris opsional yang diperlukan untuk peramalan:

> # fourier(x, K, h = NULL)

> fit <- auto.arima(cafe, xreg = fourier(cafe, K = 6),
                    seasonal = FALSE, lambda = 0)
> fit %>%
    forecast(xreg = fourier(cafe, K = 6, h = 24)) %>%
    autoplot() + ylim(1.6, 5.1)

Data gasoline yang telah dimuat sebelumnya berisi data mingguan produk bensin motor jadi di AS. Pada latihan ini, Anda akan memasang regresi harmonik pada himpunan data ini dan meramalkan 3 tahun ke depan.

Latihan ini adalah bagian dari kursus

Peramalan di R

Petunjuk latihan

Siapkan matriks xreg bernama harmonics menggunakan metode fourier() pada gasoline dengan orde \(K=13\) yang dipilih untuk meminimalkan AICc.
Pasang model regresi dinamis ke fit. Atur xreg sama dengan harmonics dan seasonal menjadi FALSE karena musiman ditangani oleh peubah penjelas.
Siapkan matriks xreg baru bernama newharmonics dengan cara serupa, lalu hitung peramalan untuk tiga tahun ke depan sebagai fc.
Terakhir, plot peramalan fc.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set up harmonic regressors of order 13
harmonics <- fourier(___, K = ___)

# Fit regression model with ARIMA errors
fit <- auto.arima(___, xreg = ___, seasonal = ___)

# Forecasts next 3 years
newharmonics <- fourier(___, K = ___, h = ___)
fc <- forecast(___, xreg = ___)

# Plot forecasts fc
___

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: 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: Regresi dinamis Exercise 2: Meramalkan penjualan dengan mempertimbangkan pengeluaran iklan Exercise 3: Peramalan permintaan listrik Exercise 4: Regresi harmonik dinamis Exercise 5: Peramalan data mingguan

Latihan Saat Ini

Exercise 6: Regresi harmonik untuk multi-musiman Exercise 7: Peramalan pemesanan panggilan Exercise 8: Model TBATS Exercise 9: Model TBATS untuk permintaan listrik Exercise 10: Masa depan Anda dalam peramalan!