Sesuaikan model musiman campuran

Ketergantungan musiman murni seperti yang dibahas sebelumnya di bab ini relatif jarang. Sebagian besar deret waktu musiman memiliki ketergantungan campuran, artinya hanya sebagian variasi yang dijelaskan oleh tren musiman.

Ingat bahwa model musiman lengkap dinyatakan sebagai SARIMA(p,d,q)x(P,D,Q)_S di mana huruf kapital menyatakan orde musiman.

Seperti sebelumnya, latihan ini meminta Anda membandingkan pasangan P/ACF sampel dengan nilai sebenarnya untuk beberapa data musiman tersimulasi dan menyesuaikan model ke data menggunakan sarima(). Kali ini, data yang disimulasikan berasal dari model musiman campuran, SARIMA(0,0,1)x(0,0,1)₁₂. Plot menampilkan tiga tahun data, serta ACF dan PACF model. Perhatikan bahwa, berbeda dengan model musiman murni, terdapat korelasi pada lag nonmusiman maupun lag musiman.

Seperti biasa, paket astsa sudah dimuat. Data yang dihasilkan ada di x.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Plot ACF dan PACF sampel dari data yang dihasilkan hingga lag 60 (max.lag = 60) dan bandingkan dengan nilai sebenarnya.
Sesuaikan model ke data yang dihasilkan (x) menggunakan sarima(). Seperti pada latihan sebelumnya, pastikan Anda menentukan argumen musiman tambahan dalam perintah sarima() Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot sample P/ACF pair to lag 60 and compare to actual


# Fit the seasonal model to x

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 Exercise 4: Model musiman campuran Exercise 5: Sesuaikan model musiman campuran

Latihan Saat Ini

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!