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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

Concepts

Machine Learning

Author (1):

Ben Auffarth

Table of Contents (15) Chapters

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

Python libraries

There are a few popular libraries for classical time-series modeling in Python, but the most popular by far is statsmodels. The following chart compares the popularity of libraries in terms of the number of stars on GitHub:

Figure 5.3: Popularity of Python libraries for classical time-series forecasting

Statsmodels is clearly the most popular among these libraries. I've only chosen to include libraries that are actively maintained and that implement the algorithms directly rather than importing them from other libraries. The SkTime or Darts libraries, for example, offer traditional forecasting models, but they are not implemented there, but in statsmodels.

pmdarima (originally pyramid-arima) contains a parameter search to help fit the best ARIMA model to univariate time-series. Anticipy contains a number of models, such as exponential decay and step models. Arch implements tools for financial econometrics and functionality for Autoregressive...

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Authors (1)

Ben Auffarth

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.

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