# ARIMA Models in Python

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

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

Free

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.

Intro to time series and stationarity
50 xp
Exploration
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!

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.

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.

In the following tracks

Time Series

Collaborators

#### 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.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden