Tanımlama II

savings zaman serisinin fark almadan durağan olduğunu öğrendin. Artık bu bilgiyle hangi model derecesinin en iyi uyumu sağlayacağını belirlemeyi deneyebilirsin.

plot_acf() ve plot_pacf() fonksiyonları içe aktarıldı ve zaman serisi savings adlı DataFrame'e yüklendi.

Bu egzersiz

Python'da ARIMA Modelleri

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Gecikmeler 1-10 için ACF grafiği oluştur ve ax1 eksenine çiz.
PACF için de aynısını yap.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create figure
fig, (ax1, ax2) = plt.subplots(2,1, figsize=(12,8))
 
# Plot the ACF of savings on ax1
____

# Plot the PACF of savings on ax2
____

plt.show()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da ARIMA Modelleri

kursunun bir parçasıdır

AvançadoNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: ACF ve PACF'ye Giriş Exercise 2: AR mı yoksa MA mı Exercise 3: Depremlerin mertebesi Exercise 4: AIC ve BIC’e Giriş Exercise 5: Model derecesi üzerinde arama Exercise 6: AIC ve BIC ile derece seçimi Exercise 7: AIC ve BIC vs ACF ve PACF Exercise 8: Model tanılaması Exercise 9: Ortalama mutlak hata Exercise 10: Tanısal özet istatistikleri Exercise 11: Teşhis grafikleri Exercise 12: Box-Jenkins yöntemi Exercise 13: Tanımlama Exercise 14: Tanımlama II

Geçerli Egzersiz

Exercise 15: Tahmin Exercise 16: Teşhis

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: Seasonal time series Exercise 2: Seasonal decompose Exercise 3: Seasonal ACF and PACF Exercise 4: SARIMA models Exercise 5: Fitting SARIMA models Exercise 6: Choosing SARIMA order Exercise 7: SARIMA vs ARIMA forecasts Exercise 8: Automation and saving Exercise 9: Automated model selection Exercise 10: Saving and updating models Exercise 11: SARIMA and Box-Jenkins Exercise 12: Multiplicative vs additive seasonality Exercise 13: SARIMA model diagnostics Exercise 14: SARIMA forecast Exercise 15: Congratulations!