Paskan model murni musiman

Seperti pada model lainnya, Anda dapat memasang model musiman di R menggunakan perintah sarima() dalam paket astsa.

Untuk memahami cara kerja model murni musiman, sebaiknya gunakan data simulasi. Kami menghasilkan 250 observasi dari model murni musiman yang diberikan oleh $$X_t = .9 X_{t-12} + W_t + .5 W_{t-12}\,,$$ yang kami nyatakan sebagai SARMA(P = 1, Q = 1)_{S = 12}. Tiga tahun data serta ACF dan PACF model telah dipetakan untuk Anda.

Anda akan membandingkan nilai ACF dan PACF sampel dari data yang dihasilkan dengan nilai sebenarnya yang ditampilkan.

Paket astsa telah dimuat untuk Anda dan data hasil generasi ada di x.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Gunakan acf2() untuk memplot ACF dan PACF sampel dari data yang dihasilkan hingga lag 60 dan bandingkan dengan nilai sebenarnya. Untuk mengestimasi hingga lag 60, setel argumen max.lag sama dengan 60.
Pasangkan model ke data yang dihasilkan menggunakan sarima(). Selain argumen p, d, dan q pada perintah sarima() Anda, tentukan juga P, D, Q, dan S (perhatikan bahwa R peka huruf besar/kecil).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot sample P/ACF to lag 60 and compare to the true values
acf2(___, max.lag = ___)

# Fit the seasonal model to x
sarima(x, p = 0, d = 0, q = 0, P = ___, D = 0, Q = ___, S = ___)

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 - integrated ARMA Exercise 2: ARIMA - plug and play Exercise 3: Simulated ARIMA Exercise 4: Global warming Exercise 5: ARIMA diagnostics Exercise 6: Diagnostics - simulated overfitting Exercise 7: Diagnostics - global temperatures Exercise 8: Forecasting ARIMA Exercise 9: Forecasting simulated ARIMA Exercise 10: Forecasting global temperatures

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: Model musiman murni Exercise 2: P/ACF untuk model musiman murni Exercise 3: Paskan model murni musiman

Latihan Saat Ini

Exercise 4: Model musiman campuran Exercise 5: Sesuaikan model musiman campuran Exercise 6: Analisis data - pengangguran I Exercise 7: Analisis data - pengangguran II Exercise 8: Analisis data - harga komoditas Exercise 9: Analisis data - tingkat kelahiran Exercise 10: Peramalan ARIMA musiman Exercise 11: Peramalan pengangguran bulanan Exercise 12: Seberapa sulit memproyeksikan harga komoditas?Exercise 13: Selamat!