Menyesuaikan model MA(1)

Dalam latihan ini, kami menghasilkan data dari model MA(1), $$X_t = W_t - .8 W_{t-1} ,$$ x <- arima.sim(model = list(order = c(0, 0, 1), ma = -.8), n = 100). Amati data hasil simulasi serta ACF dan PACF sampelnya untuk menentukan orde berdasarkan tabel yang diberikan pada latihan pertama. Lalu sesuaikan modelnya.

Ingat bahwa untuk model MA(q) murni, ACF teoretis akan terputus pada lag q, sedangkan PACF akan meluruh.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Paket astsa sudah dimuat. Sebanyak 100 observasi MA(1) telah dimuat sebagai x.
Gunakan plot() untuk membuat plot data yang dihasilkan dalam x.
Plot pasangan ACF dan PACF sampel menggunakan acf2() dari paket astsa.
Gunakan sarima() dari astsa untuk menyesuaikan MA(1) pada data yang telah dihasilkan sebelumnya. Periksa t-table dan bandingkan estimasinya dengan nilai sebenarnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# astsa is preloaded

# Plot x


# Plot the sample P/ACF of x


# Fit an MA(1) to the data and examine the t-table

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: Model AR dan MA Exercise 2: Memfitting model AR(1)Exercise 3: Menyesuaikan model AR(2)Exercise 4: Menyesuaikan model MA(1)

Latihan Saat Ini

Exercise 5: AR dan MA bersama-sama Exercise 6: Memasang model ARMA Exercise 7: Identifikasi model ARMA Exercise 8: Pemilihan model dan analisis residual Exercise 9: Pemilihan model - I Exercise 10: Pemilihan model - II Exercise 11: Analisis residual - I Exercise 12: Analisis residual - II Exercise 13: Masuk ARMA

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: 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!