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DataCamp

ARIMA Models in Python

via DataCamp

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Overview

Learn about ARIMA models in Python and become an expert in time series analysis.

Have you ever tried to predict the future? What lies ahead is a mystery which is usually only solved by waiting. In this course, you will stop waiting and learn to use the powerful ARIMA class models to forecast the future. You will learn how to use the statsmodels package to analyze time series, to build tailored models, and to forecast under uncertainty. How will the stock market move in the next 24 hours? How will the levels of CO2 change in the next decade? How many earthquakes will there be next year? You will learn to solve all these problems and more.

Syllabus

  • 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.
  • 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!
  • 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.
  • 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.

Taught by

James Fulton

Reviews

4.0 rating, based on 1 Class Central review

4.8 rating at DataCamp based on 22 ratings

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  • Profile image for Atila Mahnesaei
    Atila Mahnesaei
    The "ARIMA Models in Python" course is a comprehensive and informative resource for anyone interested in time series analysis and forecasting. The course provides a detailed explanation of ARIMA models, their components, and how to implement them using Python. The instructor's explanations are clear, and the examples are well-structured, making it easy to follow along and apply the concepts in real-world scenarios. The course also covers important topics like model diagnostics, parameter estimation, and model selection, enhancing the overall learning experience. I highly recommend this course to anyone looking to expand their knowledge of ARIMA modeling in Python.

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