Peramalan ARIMA tersimulasi

Sekarang Anda telah mahir memasangkan model ARIMA, Anda dapat menggunakan keterampilan tersebut untuk melakukan peramalan. Pertama, Anda akan bekerja dengan data tersimulasi.

Kami menghasilkan 120 observasi dari model ARIMA(1,1,0) dengan parameter AR 0,9. Datanya berada di y dan 100 observasi pertama berada di x. Observasi ini telah diplot untuk Anda. Anda akan memasangkan model ARIMA(1,1,0) pada data di x dan memverifikasi bahwa modelnya sesuai dengan baik. Lalu gunakan sarima.for() dari astsa untuk meramalkan data 20 periode waktu ke depan. Anda kemudian akan membandingkan hasil peramalan dengan data aktual di y.

Sintaks dasar untuk peramalan adalah sarima.for(data, n.ahead, p, d, q) di mana n.ahead adalah bilangan bulat positif yang menetapkan cakrawala peramalan. Nilai prediksi dan galat bakunya akan dicetak, data diplot berwarna hitam, dan peramalan berwarna merah disertai dua batas galat kuadrat rerata prediksi sebagai garis putus-putus biru.

Paket astsa sudah dimuat sebelumnya dan data (x) serta data berdiferensiasi (diff(x)) sudah diplot.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Plot ACF dan PACF sampel dari data yang sudah didiferensiasi untuk menentukan model.
Gunakan sarima() untuk memasangkan ARIMA(1,1,0) pada data. Periksa keluaran perintah sarima() Anda untuk menilai kesesuaian dan diagnostik model.
Gunakan sarima.for() untuk meramalkan data 20 periode waktu ke depan. Bandingkan dengan nilai aktual.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot P/ACF pair of differenced data 


# Fit model - check t-table and diagnostics


# Forecast the data 20 time periods ahead
sarima.for(x, n.ahead = ___, p = ___, d = ___, q = ___) 
lines(y)

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 - ARMA terintegrasi Exercise 2: ARIMA - siap pakai Exercise 3: ARIMA Tersimulasi Exercise 4: Pemanasan global Exercise 5: Diagnostik ARIMA Exercise 6: Diagnostik - overfitting terstimulasi Exercise 7: Diagnostik - suhu global Exercise 8: Peramalan ARIMA Exercise 9: Peramalan ARIMA tersimulasi

Latihan Saat Ini

Exercise 10: Peramalan suhu global

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!