コース
Pythonで学ぶARIMAモデル
上級スキルレベル
更新日 2023/11
PythonMachine Learning4時間15 ビデオ57 演習4,850 XP24,857修了証明書
無料アカウントを作成
Googleで続行その他のオプションを表示または
何千もの企業の従業員が支持
チームのトレーニングを担当していますか？
Businessをお試しくださいコース説明
.
前提条件
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.
Pythonで学ぶARIMAモデル
コース完了 19百万人を超える学習者と共にPythonで学ぶARIMAモデルを始めましょう！
無料アカウントを作成
Googleで続行その他のオプションを表示または
DataCamp for Mobileでデータスキルを磨きましょう
モバイル コースと毎日の 5 分間のコーディング チャレンジで、外出先でも進歩できます。