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ARIMA Models in Python

4.7+
22 reviews
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Learn about ARIMA models in Python and become an expert in time series analysis.

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Course Description

Have you ever tried to predict the future? What lies ahead is a mystery that is usually only solved by waiting. In this course, you can stop waiting and dive into the world of time series modeling using ARIMA models in Python to forecast the future.

Time series data

Start by learning the basics of time series data, including the concept of stationarity—crucial for working with ARMA models. You'll learn how to test for stationarity both visually and statistically, generate ARMA data, and fit ARMA models to get a solid foundation.​

Statsmodels package

As you progress, explore the powerful Statsmodels package for fitting ARMA, ARIMA, and ARMAX models. You'll get hands-on experience using your models to predict future values like stock prices.

Making these concepts easy to grasp and apply, you’ll uncover generating one-step-ahead predictions, dynamic forecasts, and fitting ARIMA models directly to your data.

ACF and PACF plots

One of the highlights is learning how to choose the best model using ACF and PACF plots to identify promising model orders. You'll learn about criteria like AIC and BIC for model selection and diagnostics, helping you refine your models to perfection​​.

SARIMA models

The course wraps up with seasonal ARIMA (SARIMA) models, perfect for handling data with seasonal patterns. You'll learn to decompose time series data into seasonal and non-seasonal components and apply your ARIMA skills in a global forecast challenge.

This final project ties everything together, giving you a comprehensive understanding of ARIMA modeling.
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In the following Tracks

Time Series in Python

Go To Track
  1. 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.

    Play Chapter Now
    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. 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.

    Play Chapter Now
  3. 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.

    Play Chapter Now
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Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Time Series in Python

Go To Track

datasets

US Monthly Candy ProductionMonthly Record of CO2Amazon Daily Closing Stock PriceMonthly Milk ProductionYearly Earthquakes

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Adel Nehme

audio recorded by

James Fulton's avatar
James Fulton
James Fulton HeadshotJames 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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Don’t just take our word for it

*4.7
from 22 reviews
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5%
  • Flavio Z.
    3 months

    Nice course. Straight to the point. Always clear.

  • Saloua C.
    4 months

    I was looking for a course on classical models for time series forecasting that provides a thorough explanation of the theory and practical application on real data. This was the only course I found that met those criteria. It was very well-structured and explained. I especially appreciated the practical exercises that helped reinforce the material. Thank you very much! It would be great if more advanced models, such as those for handling double seasonality, could also be included.

  • Desislava B.
    10 months

    All important concepts very well explained, it helped me with new approches that I later used on my own data

  • Jordan B.
    about 1 year

    Good

  • Nadiia D.
    about 1 year

    During the whole track before I was not able to catch the main idea of AR, MA and ARIMA models. This course gives just brilliant explanations that are well structured. Thank you a lot for this one 🙏🏼

"Nice course. Straight to the point. Always clear."

Flavio Z.

"All important concepts very well explained, it helped me with new approches that I later used on my own data"

Desislava B.

"Good"

Jordan B.

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