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Machine Learning for Finance

You're reading from   Machine Learning for Finance Principles and practice for financial insiders

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789136364
Length 456 pages
Edition 1st Edition
Languages
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Authors (2):
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Jannes Klaas Jannes Klaas
Author Profile Icon Jannes Klaas
Jannes Klaas
James Le James Le
Author Profile Icon James Le
James Le
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Table of Contents (15) Chapters Close

Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
1. Neural Networks and Gradient-Based Optimization 2. Applying Machine Learning to Structured Data FREE CHAPTER 3. Utilizing Computer Vision 4. Understanding Time Series 5. Parsing Textual Data with Natural Language Processing 6. Using Generative Models 7. Reinforcement Learning for Financial Markets 8. Privacy, Debugging, and Launching Your Products 9. Fighting Bias 10. Bayesian Inference and Probabilistic Programming Index

VAEs for time series


This section covers the how and why of time series VAEs and gives a couple of examples where they have been used. Time series are such a big topic in finance that Chapter 4, Understanding Time Series, is heavily focused to it.

Autoencoders have found applications in connection to time series as they are able to encode a long time series into a single, descriptive vector. This vector can then, for example, be used to efficiently compare one time series to another time series, based on specific and complex patterns that cannot be captured with a simple correlation, for instance.

Consider the 2010 "Flash Crash." On May 6, 2010, starting at 02:32, US markets saw a major loss of value. The Dow Jones Industrial Average lost about 9%, which equates to about a trillion dollars' worth of value being wiped out in a couple of minutes. 36 minutes later, the crash was over, most of the lost value was regained, and people started wondering what on earth had just happened.

Five years...

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