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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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Toc

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

Autocorrelation


Autocorrelation is the correlation between two elements of a series separated by a given interval. Intuitively, we would, for example, assume that knowledge about the last time step helps us in forecasting the next step. But how about knowledge from 2 time steps ago or from 100 time steps ago?

Running autocorrelation_plot will plot the correlation between elements with different lag times and can help us answer these questions. As a matter of fact, pandas comes with a handy autocorrelation plotting tool. To use it, we have to pass a series of data. In our case, we pass the page views of a page, selected at random.

We can do this by running the following code:

from pandas.plotting import autocorrelation_plot

autocorrelation_plot(data.iloc[110])
plt.title(' '.join(train.loc[110,['Subject', 'Sub_Page']]))

This will present us with the following diagram:

Autocorrelation of the Oh My Girl Chinese Wikipedia page

The plot in the preceding chart shows the correlation of page views for...

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