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Python으로 배우는 ARIMA 모델
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업데이트됨 2023. 11.
PythonMachine Learning4시간15 동영상57 연습 문제4,850 XP24,862성취 증명서
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선수 조건
Supervised Learning with scikit-learn1
ARMA Models
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.
2
Fitting the Future
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!
3
The Best of the Best Models
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.
4
Seasonal ARIMA Models
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.
Python으로 배우는 ARIMA 모델
강의 완료
19백만 명 이상의 학습자와 함께 Python으로 배우는 ARIMA 모델을(를) 시작하세요!
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