Peramalan suhu global

Sekarang Anda dapat mencoba melakukan peramalan pada data nyata.

Di sini, Anda akan meramalkan deviasi suhu global tahunan globtemp hingga tahun 2050. Ingat bahwa pada latihan sebelumnya, Anda memasangkan model ARIMA(0,1,2) pada data tersebut. Anda akan memasangkan ulang modelnya untuk mengonfirmasi, lalu meramalkan deret tersebut 35 tahun ke depan.

Paket astsa sudah dimuat dan datanya sudah diplot.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Pasangkan model ARIMA(0,1,2) pada data menggunakan sarima(). Berdasarkan analisis Anda sebelumnya, ini adalah model terbaik untuk data globtemp. Periksa kembali signifikansi parameter pada keluaran t-table dan periksa residual untuk melihat adanya penyimpangan dari asumsi model.
Gunakan sarima.for() untuk meramalkan data suhu global Anda 35 tahun ke depan hingga 2050 menggunakan hasil pemodelan ARIMA(0,1,2).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Fit an ARIMA(0,1,2) to globtemp and check the fit


# Forecast data 35 years into the future

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 Exercise 10: Peramalan suhu global

Latihan Saat Ini

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!