ARIMA Tersimulasi

Sebelum menganalisis deret waktu aktual, Anda sebaiknya mencoba bekerja dengan model yang sedikit lebih rumit.

Di sini, kami menghasilkan 250 pengamatan dari model ARIMA(2,1,0) dengan drift yang diberikan oleh $$Y_t = 1 + 1.5 Y_{t-1} - .75 Y_{t-2} + W_t\,$$ dengan \(Y_t = \nabla X_t = X_{t} - X_{t-1}\).

Anda akan menggunakan teknik yang sudah dipelajari untuk memasangkan model ke data.

Paket astsa sudah dimuat, dan data yang dihasilkan ada di x. Deret x dan deret yang sudah didiferensiasikan y <- diff(x) telah diplot.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Plot ACF dan PACF sampel menggunakan acf2() pada data yang telah didiferensiasikan diff(x) untuk menentukan model.
Pasangkan model ARIMA(2,1,0) menggunakan sarima() pada data yang dihasilkan. Periksa t-table dan informasi keluaran lainnya untuk menilai kecocokan model.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot sample P/ACF of differenced data and determine model



# Estimate parameters and examine output

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model ARIMA di R

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Exercise 1: First things first Exercise 2: Data play Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

Exercise 1: AR and MA models Exercise 2: Fitting an AR(1) model Exercise 3: Fitting an AR(2) model Exercise 4: Fitting an MA(1) model Exercise 5: AR and MA together Exercise 6: Fitting an ARMA model Exercise 7: Identify an ARMA model Exercise 8: Model choice and residual analysis Exercise 9: Model choice - I Exercise 10: Model choice - II Exercise 11: Residual analysis - I Exercise 12: Residual analysis - II Exercise 13: ARMA get in

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

Exercise 1: ARIMA - ARMA terintegrasi Exercise 2: ARIMA - siap pakai Exercise 3: ARIMA Tersimulasi

Latihan Saat Ini

Exercise 4: Pemanasan global Exercise 5: Diagnostik ARIMA Exercise 6: Diagnostik - overfitting terstimulasi Exercise 7: Diagnostik - suhu global Exercise 8: Peramalan ARIMA Exercise 9: Peramalan ARIMA tersimulasi Exercise 10: Peramalan suhu global

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Exercise 1: Pure seasonal models Exercise 2: P/ACF of pure seasonal models Exercise 3: Fit a pure seasonal model Exercise 4: Mixed seasonal models Exercise 5: Fit a mixed seasonal model Exercise 6: Data analysis - unemployment I Exercise 7: Data analysis - unemployment II Exercise 8: Data analysis - commodity prices Exercise 9: Data analysis - birth rate Exercise 10: Forecasting seasonal ARIMA Exercise 11: Forecasting monthly unemployment Exercise 12: How hard is it to forecast commodity prices?Exercise 13: Congratulations!