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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
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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

Fast Fourier transformations


Another interesting statistic we often want to compute about time series is the Fourier transformation (FT). Without going into the math, a Fourier transformation will show us the amount of oscillation within a particular frequency in a function.

You can imagine this like the tuner on an old FM radio. As you turn the tuner, you search through different frequencies. Every once in a while, you find a frequency that gives you a clear signal of a particular radio station. A Fourier transformation basically scans through the entire frequency spectrum and records at what frequencies there is a strong signal. In terms of a time series, this is useful when trying to find periodic patterns in the data.

Imagine that we found out that a frequency of one per week gave us a strong pattern. This would mean that knowledge about what the traffic was ton the same day one week ago would help our model.

When both the function and the Fourier transform are discrete, which is the case...

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