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Machine Learning for Time-Series with Python

You're reading from Machine Learning for Time-Series with Python Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Product type Paperback

Published in Jun 2026

Publisher Packt

ISBN-13 9781837631339

Length 397 pages

Edition 2nd Edition

Concepts

Machine Learning

Author (1):

Ben Auffarth

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Table of Contents (3) Chapters

1. Machine Learning for Time-Series with Python, Second Edition: Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods

2. Introduction FREE CHAPTER

3. Dealing with Time Series in Python

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In this chapter, we’ll introduce working with time series data in the Python programming language. We will cover how to install Python and libraries in Python. This chapter will help you set up everything you need to get started with the book.

Python has become one of the most popular languages for data science. It is a powerful, easy-to-read, general-purpose high-level programming language. Additionally, the Python community has built a large number of useful packages for data analysis, which can be easily installed using the pip package manager

The most popular Python packages for data science are collected in the so-called SciPy stack, which includes packages such as NumPy, pandas, scikit-learn, and matplotlib.

In this chapter, we’ll start by installing Python and lots of libraries. Then we will introduce pandas, NumPy, and matplotlib, and a few other libraries and modules that will come in...

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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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