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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

What Is Preprocessing?

Anyone who's ever worked in a company on a machine learning project knows that real-world data is messy. It's often aggregated from multiple sources or using multiple platforms or recording devices, and it's incomplete and inconsistent. In preprocessing, we want to improve the data quality to successfully apply a machine learning model.

Data preprocessing includes the following set of techniques:

  • Feature transforms
    • Scaling
    • Power/log transforms
    • Imputation
  • Feature engineering

These techniques fall largely into two classes: either they tailor to the assumptions of the machine learning algorithm (feature transforms) or they are concerned with constructing more complex features from multiple underlying features (feature engineering). We'll only deal with univariate feature transforms, transforms that apply to one feature at a time. We won't discuss multivariate feature...

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