Dinamik tahminler üretme

Şimdi biraz daha ileriye, dinamik tahminlere gidelim. Diyelim ki Amazon hisse fiyatını sadece yarın için değil, gelecek hafta ya da gelecek ay için de tahmin etmek istiyorsun. İşte burada dinamik tahminler devreye girer.

Videoda öğrendiğin gibi, uzun vadeli tahminleri hassas yapmak daha zordur çünkü şok terimleri birikir. Tahminler ne kadar geleceğe giderse, belirsizlik o kadar artar. Bu özellikle hisse senedi verilerinde geçerlidir, bu yüzden bu egzersizdeki tahminlerinin bir önceki egzersizdeki kadar isabetli olmadığını görebilirsin.

Bu egzersiz

Python'da ARIMA Modelleri

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

results nesnesini kullanarak son 30 gün için dinamik tahmin yap ve sonucu dynamic_forecast değişkenine ata.
Tahminlerini, dynamic_forecast nesnesinin bir özniteliğini kullanarak mean_forecast adlı yeni bir değişkene ata.
Tahminlerinin güven aralıklarını dynamic_forecast nesnesinden çıkar ve confidence_intervals adlı yeni bir değişkene ata.
Ortalama tahminlerini yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Generate predictions
dynamic_forecast = results.______(____=___, ____=____)

# Extract prediction mean
mean_forecast = ____.____

# Get confidence intervals of predictions
confidence_intervals = ____.____

# Select lower and upper confidence limits
lower_limits = confidence_intervals.loc[:,'lower close']
upper_limits = confidence_intervals.loc[:,'upper close']

# Print best estimate predictions
print(____)

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: Zaman serisi modellerini uydurma Exercise 2: AR ve MA modellerini uydurma Exercise 3: Bir ARMA modeli uydurma Exercise 4: Bir ARMAX modeli uydurma Exercise 5: Tahminleme Exercise 6: Bir adım ileriden tahminler üretme Exercise 7: Bir adım ilerisi tahminlerini görselleştirme Exercise 8: Dinamik tahminler üretme

Geçerli Egzersiz

Exercise 9: Dinamik tahminleri görselleştirme Exercise 10: ARIMA modellere giriş Exercise 11: Fark alma ve ARMA uydurma Exercise 12: ARMA tahminini açmak Exercise 13: Bir ARIMA modeli uyarlama Exercise 14: ARIMA modeli seçimi

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: 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!