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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

Preprocessing Time-Series

Preprocessing is a crucial step in machine learning that is nonetheless often neglected. Many books don't cover preprocessing in any depth or skip preprocessing entirely. When presenting to outsiders about a machine learning project, curiosity is naturally attracted to the algorithm rather than the dataset or the preprocessing.

One reason for the relative silence on preprocessing could be that it's less glamorous than machine learning itself. It is, however, often the step that takes the most time, sometimes estimated at around 98% of the whole machine learning process. And it is often in preprocessing that relatively easy work can have a great impact on the eventual performance of the machine learning model. The quality of the data goes a long way toward determining the outcome – low-quality input, in the worst case, can invalidate the machine learning work altogether (this is summarized in the adage "garbage in, garbage out...

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