Autokorelasi deret waktu non-musiman

Cara lain untuk melihat data deret waktu adalah dengan memetakan setiap observasi terhadap observasi lain yang terjadi beberapa waktu sebelumnya menggunakan gglagplot(). Sebagai contoh, Anda dapat memetakan \(y_t\) terhadap \(y_{t-1}\). Ini disebut plot lag karena Anda memetakan deret waktu terhadap lag dari dirinya sendiri.

Korelasi yang terkait dengan plot lag membentuk apa yang disebut autocorrelation function (ACF). Fungsi ggAcf() menghasilkan plot ACF.

Dalam latihan ini, Anda akan bekerja dengan data oil yang sudah dimuat (tersedia dalam paket fpp2), yang berisi produksi minyak tahunan di Arab Saudi dari 1965–2013 (diukur dalam juta ton).

Latihan ini adalah bagian dari kursus

Peramalan di R

Petunjuk latihan

Gunakan fungsi autoplot() untuk memetakan data oil.
Untuk data oil, petakan hubungan antara \(y_t\) dan \(y_{t-k}\), \(k=1,\dots,9\) menggunakan salah satu dari dua fungsi yang diperkenalkan di atas. Perhatikan bagaimana hubungan tersebut berubah seiring bertambahnya lag.
Demikian pula, petakan korelasi yang terkait dengan setiap plot lag menggunakan fungsi baru lain yang sesuai.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create an autoplot of the oil data
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# Create a lag plot of the oil data
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# Create an ACF plot of the oil data
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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: Selamat Datang di Peramalan Menggunakan R Exercise 2: Membuat objek deret waktu di R Exercise 3: Plot deret waktu Exercise 4: Plot musiman Exercise 5: Tren, musiman, dan siklus Exercise 6: Autokorelasi deret waktu non-musiman

Latihan Saat Ini

Exercise 7: Autokorelasi deret waktu musiman dan siklik Exercise 8: Cocokkan ACF dengan deret waktunya Exercise 9: White noise Exercise 10: Harga saham dan 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: 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!