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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

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Product type Paperback
Published in Jun 2026
Publisher Packt
ISBN-13 9781837631339
Length 397 pages
Edition 2nd 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 (3) Chapters Close

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

Working with time series in Python

In this section, we’ll give a brief introduction to Python and its ecosystem for time series. We’ll go through a few of the basics with data loading for time series and plotting.

Python has a lot of tools for time series, such as datetime, time, and calendar. At the same time, there are many different data types which can be highly confusing for beginners.

These are important data types for time series:

  • datetime: This is a Python standard library class. It represents a single moment in time.
  • date: This is a Python standard library class. It represents a single day.
  • time: This is a Python standard library class. It represents a single time of day.
  • timedelta: This is a Python standard library class. It represents a duration of time.
  • tzinfo: This is a Python standard library class. It represents a time zone.
  • relativedelta: This is a third-party library class. It represents a duration of time relative to another datetime.

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