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ARIMA Models in Python

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

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4 Hours15 Videos57 Exercises12,494 Learners4850 XPTime Series Track

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Course Description

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.

  1. 1

    Chapter 1 - 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.

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    Intro to time series and stationarity
    50 xp
    100 xp
    Train-test splits
    100 xp
    Is it stationary
    100 xp
    Making time series stationary
    50 xp
    Augmented Dicky-Fuller
    100 xp
    Taking the difference
    100 xp
    Other tranforms
    100 xp
    Intro to AR, MA and ARMA models
    50 xp
    Model order
    100 xp
    Generating ARMA data
    100 xp
    Fitting Prelude
    100 xp
  2. 2

    Chapter 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!

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  3. 3

    Chapter 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.

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  4. 4

    Chapter 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.

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In the following tracks

Time Series


chesterChester IsmayAAN94Adel Nehme
James Fulton Headshot

James Fulton

Climate Informatics Researcher

James is a PhD researcher at the University of Edinburgh, where he tutors computing, machine learning, data analysis, and statistical physics. His research involves using and developing machine learning algorithms to extract space-time patterns from climate records and climate models. He has held visiting researcher roles, working on planet-scale data analysis and modeling, at the University of Oxford and Queen's University Belfast and has a masters in physics where he specialized in quantum simulation. In a previous life, he was employed as a data scientist in the insurance sector. When not several indents deep in Python, he performs improvised comedy.
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