Eksplorasi data

Dalam video, Anda melihat berbagai jenis data. Pada latihan ini, Anda akan memplot data deret waktu tambahan dan membandingkannya dengan yang Anda lihat di video. Ada baiknya memikirkan bagaimana deret waktu ini dibandingkan dengan deret pada video. Secara khusus, fokuslah pada jenis tren, musiman atau periodisitas, dan homoskedastisitas.

Sebelum Anda menggunakan suatu himpunan data untuk pertama kali, sebaiknya gunakan sistem bantuan untuk melihat detail datanya. Misalnya, gunakan help(AirPassengers) atau ?AirPassengers untuk melihat detail deret tersebut.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Paket astsa dan xts telah dimuat sebelumnya di lingkungan R Anda.
Gunakan help() untuk melihat rincian berkas data AirPassengers.
Gunakan plot() untuk memplot data penumpang maskapai (AirPassengers) dan bandingkan dengan deret yang Anda lihat di video.
Plot penutupan harian DJIA (djia$Close) dan bandingkan dengan deret yang Anda lihat di video.
Plot Southern Oscillation Index (soi) dan periksa adanya tren, musiman, dan homoskedastisitas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# View a detailed description of AirPassengers


# Plot AirPassengers


# Plot the DJIA daily closings


# Plot the Southern Oscillation Index

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: Pertama-tama Exercise 2: Eksplorasi data

Latihan Saat Ini

Exercise 3: Unsur-unsur deret waktu Exercise 4: Kestasioneran dan ketidakstasioneran Exercise 5: Pembedaan (Differencing)Exercise 6: Menghapus tren data 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!