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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

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

Summary

In this chapter, we've introduced time-series, the history of research into time-series, and Python for time-series.

We started with a definition of time-series and its main properties. We then looked at the history of the study of time-series in different scientific disciplines, such as demography and genetics, astronomy, economics, meteorology, medicine, and applied statistics.

Then, we went over the capabilities of Python for time-series and why Python is the go-to language for doing machine learning with time-series. Finally, I described how to install and use Python for time-series analysis and machine learning, and we covered some of the basics of Python as relevant to time-series and machine learning.

In the next chapter, we'll look at time-series analysis with Python.

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