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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

Fuzzy Modeling

Fuzzy logic and fuzzy set theory were developed by Lotfi Zadeh in the 1960s and 70s while a professor at the University of California, Berkeley. Born to Persian and Jewish Russian parents in Baku, Azerbaijan, he completed his schooling in Tehran, Iran, and later moved to the USA, where he studied at MIT and Columbia. As a result, he was familiar with how concepts are understood in different cultures and expressed in different languages. This inspired his research approach to approximate reasoning and linguistic variables that he formalized as fuzzy theory.

Fuzzy set theory is an approach that can deal with problems relating to ambiguous, subjective, and imprecise judgments. Vagueness is inherent in everyday language, and fuzziness was invented to express this and work with it in an intuitive manner. Fuzzy logic expresses subjective belief and vagueness. It can and has been claimed that probability theory is a subset of fuzzy logic.

Fuzzy sets are sets whose...

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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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