Menghapus tren data

Seperti yang telah Anda lihat pada latihan sebelumnya, pembedaan umumnya efektif untuk menghilangkan tren dari data deret waktu. Ingat bahwa pembedaan melihat selisih antara nilai deret waktu pada titik waktu tertentu dan nilai sebelumnya.

Pada latihan ini, Anda akan menggunakan fungsi pembedaan diff() untuk menghilangkan tren dan memplot data deret waktu nyata.

Latihan ini merupakan bagian dari kursus

Model ARIMA di R

Instruksi latihan

Paket astsa sudah dimuat.
Hasilkan plot multifigur yang membandingkan data suhu global (globtemp) dengan deret yang telah dihilangkan trennya. Anda dapat membuat plot multifigur dengan menjalankan perintah par() yang sudah disediakan diikuti dua pemanggilan plot() terpisah.
Hasilkan plot multifigur lain yang membandingkan mortalitas kardiovaskular mingguan di Los Angeles County (cmort) dengan deret yang telah dihilangkan trennya.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Plot globtemp and detrended globtemp
par(mfrow = c(2,1))
plot(globtemp) 


# Plot cmort and detrended cmort
par(mfrow = c(2,1))
plot(cmort)

Edit dan Jalankan Kode

Latihan ini merupakan 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: Pertama-tama Exercise 2: Eksplorasi data Exercise 3: Unsur-unsur deret waktu Exercise 4: Kestasioneran dan ketidakstasioneran Exercise 5: Pembedaan (Differencing)Exercise 6: Menghapus tren data

Latihan Saat Ini

Exercise 7: Menangani tren dan heteroskedastisitas Exercise 8: Deret waktu stasioner: ARMA Exercise 9: Mensimulasikan model ARMA

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 - 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!