Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning for Time-Series with Python

You're reading from   Machine Learning for Time-Series with Python Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Arrow left icon
Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Length 370 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python FREE CHAPTER 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

What are classical models?

In this chapter, we'll deal with models that could be characterized as having a longer tradition, and are rooted in statistics and mathematics. They are used heavily in econometrics and statistics.

While there is considerable overlap between statistics and machine learning approaches, and each community has been absorbing the work of the other, there are still a few key differences. Whereas statistics papers are still overwhelmingly formal and deductive, machine learning researchers are more pragmatic, relying on the predictive accuracy of models.

We've talked about the very early history of time-series models in Chapter 1, Introduction to Time-Series with Python. In this chapter, we'll discuss moving averages and autoregressive approaches for forecasting. These were introduced in the early 20th century and popularized by George Box and Gwilym Jenkins in 1970 in their book "Time-Series Analysis Forecasting and Control...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime