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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

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781801819626

Length 370 pages

Edition 1st Edition

Languages

Python

Tools

Prophet

Concepts

Machine Learning

Author (1):

Ben Auffarth

View More author details

Table of Contents (15) Chapters

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

Who this book is for

If you want to build models that are reactive to the latest trends, seasonality, and business cycles, this is the book for you. This book is for data scientists, analysts, or programmers who want to learn more about time-series, and want to catch up on different techniques in machine learning.

The rest of the chapter is locked

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Authors (1)

Ben Auffarth

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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