Simüle ARIMA

Gerçek zaman serisi verilerini analiz etmeden önce, biraz daha karmaşık bir modelle pratik yapman iyi olur.

Burada, sürüklenme (drift) içeren ARIMA(2,1,0) modelinden 250 gözlem ürettik: $$Y_t = 1 + 1.5 Y_{t-1} - .75 Y_{t-2} + W_t\,$$ burada \(Y_t = \nabla X_t = X_{t} - X_{t-1}\).

Veriye bir model uydurmak için öğrendiğin teknikleri kullanacaksın.

astsa paketi önceden yüklü ve üretilen veriler x içinde. x serisi ve eğilimden arındırılmış seri y <- diff(x) çizdirildi.

Bu egzersiz

R ile ARIMA Modelleri

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Modeli belirlemek için farklanmış verinin diff(x) örnek ACF ve PACF grafiklerini acf2() ile çiz.
Üretilen verilere sarima() kullanarak bir ARIMA(2,1,0) modeli uydur. Model uyumunu değerlendirmek için t-tablosunu ve diğer çıktı bilgilerini incele.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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



# Estimate parameters and examine output

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile ARIMA Modelleri

kursunun bir parçasıdır

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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 - tümleşik ARMA Exercise 2: ARIMA - tak-çalıştır Exercise 3: Simüle ARIMA

Geçerli Egzersiz

Exercise 4: Küresel ısınma Exercise 5: ARIMA tanılama Exercise 6: Tanılamalar - simüle aşırı uyum Exercise 7: Tanılama - küresel sıcaklıklar Exercise 8: ARIMA ile Tahmin Exercise 9: Simüle ARIMA ile tahmin Exercise 10: Küresel sıcaklıkların tahmin edilmesi

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!