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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

Machine learning with time-series

In this section, I'll give an introduction to applications and the main categories of machine learning with time-series.

Machine learning approaches for time-series are crucial in domains such as economics, medicine, meteorology, demography, and many others. Time-Series datasets are ubiquitous and occur in domains as diverse as healthcare, economics, social sciences, Internet-of-Things applications, operations management, digital marketing, cloud infrastructure, the simulation of robotic systems, and others. These datasets are of immense practical importance, as they can be leveraged to forecast and predict the detection of anomalies more effectively, thereby supporting decision making.

The technical applications within machine learning for time-series abound in techniques. A few applications are as follows:

  • Curve fitting
  • Regression
  • Classification
  • Forecasting
  • Segmentation/clustering
  • Anomaly detection...
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