Prakiraan SARIMA vs ARIMA

Pada latihan ini, Anda akan melihat dampak penggunaan model SARIMA dibandingkan model ARIMA terhadap prakiraan deret waktu musiman Anda.

Dua model, ARIMA(3,1,2) dan SARIMA(0,1,1)(1,1,1)\(_{12}\), telah diestimasi pada deret waktu ketenagakerjaan Wisconsin. Ini adalah model ARIMA terbaik dan model SARIMA terbaik menurut AIC.

Dalam latihan ini Anda akan menggunakan kedua model tersebut untuk membuat prakiraan dinamis 25 bulan ke depan dan memplot prediksi ini berdampingan dengan data yang disisihkan untuk periode tersebut, wisconsin_test.

Objek hasil ARIMA yang telah di-fit dan objek hasil SARIMA yang telah di-fit tersedia di lingkungan Anda sebagai arima_results dan sarima_results.

Latihan ini adalah bagian dari kursus

Model ARIMA di Python

Petunjuk latihan

Buat objek prakiraan bernama arima_pred untuk model ARIMA guna memprakirakan 25 langkah berikutnya setelah akhir data pelatihan.
Ekstrak atribut .predicted_mean dari arima_pred dan simpan ke arima_mean.
Ulangi dua langkah di atas untuk model SARIMA.
Plot prakiraan SARIMA dan ARIMA serta data yang disisihkan wisconsin_test.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create ARIMA mean forecast
arima_pred = arima_results.____
arima_mean = arima_pred.____

# Create SARIMA mean forecast
sarima_pred = sarima_results.____
sarima_mean = ____

# Plot mean ARIMA and SARIMA predictions and observed
plt.plot(dates, sarima_mean, label='SARIMA')
plt.plot(dates, arima_mean, label='ARIMA')
plt.plot(wisconsin_test, label='observed')
plt.legend()
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model ARIMA di Python

SkillTag.level.advancedSkillTag.label

4.9+

Mulai Kursus Gratis

Dive straight in and learn about the most important properties of time series. You'll learn about stationarity and how this is important for ARMA models. You'll learn how to test for stationarity by eye and with a standard statistical test. Finally, you'll learn the basic structure of ARMA models and use this to generate some ARMA data and fit an ARMA model.

Exercise 1: Intro to time series and stationarity Exercise 2: Exploration Exercise 3: Train-test splits Exercise 4: Is it stationary Exercise 5: Making time series stationary Exercise 6: Augmented Dicky-Fuller Exercise 7: Taking the difference Exercise 8: Other tranforms Exercise 9: Intro to AR, MA and ARMA models Exercise 10: Model order Exercise 11: Generating ARMA data Exercise 12: Fitting Prelude

What lies ahead in this chapter is you predicting what lies ahead in your data. You'll learn how to use the elegant statsmodels package to fit ARMA, ARIMA and ARMAX models. Then you'll use your models to predict the uncertain future of stock prices!

Exercise 1: Fitting time series models Exercise 2: Fitting AR and MA models Exercise 3: Fitting an ARMA model Exercise 4: Fitting an ARMAX model Exercise 5: Forecasting Exercise 6: Generating one-step-ahead predictions Exercise 7: Plotting one-step-ahead predictions Exercise 8: Generating dynamic forecasts Exercise 9: Plotting dynamic forecasts Exercise 10: Intro to ARIMA models Exercise 11: Differencing and fitting ARMA Exercise 12: Unrolling ARMA forecast Exercise 13: Fitting an ARIMA model Exercise 14: Choosing ARIMA model

In this chapter, you will become a modeler of discerning taste. You'll learn how to identify promising model orders from the data itself, then, once the most promising models have been trained, you'll learn how to choose the best model from this fitted selection. You'll also learn a great framework for structuring your time series projects.

Exercise 1: Intro to ACF and PACF Exercise 2: AR or MA Exercise 3: Order of earthquakes Exercise 4: Intro to AIC and BIC Exercise 5: Searching over model order Exercise 6: Choosing order with AIC and BIC Exercise 7: AIC and BIC vs ACF and PACF Exercise 8: Model diagnostics Exercise 9: Mean absolute error Exercise 10: Diagnostic summary statistics Exercise 11: Plot diagnostics Exercise 12: Box-Jenkins method Exercise 13: Identification Exercise 14: Identification II Exercise 15: Estimation Exercise 16: Diagnostics

In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge.

Exercise 1: Deret waktu musiman Exercise 2: Dekomposisi musiman Exercise 3: ACF dan PACF Musiman Exercise 4: Model SARIMA Exercise 5: Mencocokkan model SARIMA Exercise 6: Memilih orde SARIMA Exercise 7: Prakiraan SARIMA vs ARIMA

Latihan Saat Ini

Exercise 8: Otomatisasi dan penyimpanan Exercise 9: Pemilihan model otomatis Exercise 10: Menyimpan dan memperbarui model Exercise 11: SARIMA dan Box-Jenkins Exercise 12: Kemusiman multiplikatif vs aditif Exercise 13: Diagnostik model SARIMA Exercise 14: Peramalan SARIMA Exercise 15: Selamat!