MA(1) modeli uydurma

Bu egzersizde, bir MA(1) modelinden veri ürettik, $$X_t = W_t - .8 W_{t-1} ,$$ x <- arima.sim(model = list(order = c(0, 0, 1), ma = -.8), n = 100). Simüle edilen veriye, örnek ACF ve PACF’e bakarak, ilk egzersizde verilen tabloya göre derecesini belirle. Sonra modeli uydur.

Saf MA(q) modelleri için, kuramsal ACF’in gecikme q’da kesildiğini, PACF’in ise kuyruklandığını unutma.

Bu egzersiz, kursun bir parçasıdır

R ile ARIMA Modelleri

Kursa Göz Atın

Egzersiz talimatları

astsa paketi önceden yüklendi. x içinde 100 MA(1) gözlemi hazır.
plot() ile x içindeki üretilmiş veriyi görselleştir.
astsa paketindeki acf2() ile örnek ACF ve PACF çiftlerini çiz.
astsa içindeki sarima() ile daha önce üretilmiş veriye bir MA(1) uydur. t-tablosunu incele ve tahminleri gerçek değerlerle karşılaştır.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# astsa is preloaded

# Plot x


# Plot the sample P/ACF of x


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

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

R ile ARIMA Modelleri

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başla

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 ve MA modelleri Exercise 2: AR(1) modelini uydurma Exercise 3: AR(2) modeli uydurma Exercise 4: MA(1) modeli uydurma

Geçerli egzersiz

Exercise 5: AR ve MA birlikte Exercise 6: Bir ARMA modelini uydurma Exercise 7: Bir ARMA modeli belirle Exercise 8: Model seçimi ve artık analizı Exercise 9: Model seçimi - I Exercise 10: Model seçimi - II Exercise 11: Artık analizleri - I Exercise 12: Artık analizleri - II Exercise 13: ARMA devreye giriyor

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!