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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

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Length 370 pages
Edition 1st Edition
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Author (1):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
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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

Introduction to Time-Series with Python

This book is about machine learning for time-series with Python, and you can see this chapter as a 101 class for time-series. In this chapter, we'll introduce time-series, the history of research into time-series, and how to use Python for time-series.

We'll start with what a time-series is and its main properties. We'll then look at the history of the study of time-series in different scientific disciplines foundational to the field, such as demography, astronomy, medicine, and economics.

Then, we'll go 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 will describe how to install the most prominent libraries in Python for time-series analysis and machine learning, and we'll cover the basics of Python as relevant to time-series and machine learning.

We're going to cover the following topics:

    ...
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