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Machine Learning for Time-Series with Python

You're reading from  Machine Learning for Time-Series with Python

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Ben Auffarth Ben Auffarth
Profile icon Ben Auffarth
Toc

Table of Contents (15) Chapters close

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python 3. Preprocessing Time-Series 4. Introduction to Machine Learning for Time-Series 5. Forecasting with Moving Averages and Autoregressive Models 6. Unsupervised Methods for Time-Series 7. Machine Learning Models for Time-Series 8. Online Learning for Time-Series 9. Probabilistic Models for Time-Series 10. Deep Learning for Time-Series 11. Reinforcement Learning for Time-Series 12. Multivariate Forecasting 13. Other Books You May Enjoy
14. Index

Forecasting with Moving Averages and Autoregressive Models

This chapter is about time-series modeling based on moving averages and autoregression. This comprises a large set of models that are very popular in different disciplines, including econometrics and statistics. We'll discuss autoregression and moving averages models, along with others that combine these two, such as ARMA, ARIMA, VAR, GARCH, and others.

These models are still held in high esteem and find their applications. However, many new models have since sprung up that have been shown to be competitive or even outperform these simpler ones. Within their main application, however, in univariate forecasting, simple models often provide accurate or accurate enough predictions, so that these models constitute a mainstay in time-series modeling.

We're going to cover the following topics:

  • What are classical models?
    • Moving average and autoregression
    • Model selection and...
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