Finding patterns in timed data with spike timing dependent plasticity
Name
825818413-MIT.pdf
Description
Full printable version
Size
8.25 MB
Format
Adobe PDF
Checksum (MD5)
2f7405f2c8246635e74296d1966b3bb3
Author(s)
Oliveira, Alexandre (Alexandre S.)
Advisor(s)
Patrick H. Winston and Victor Chan.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
My research focuses on finding patterns in events - in sequences of data that happen over time. It takes inspiration from a neuroscience phenomena believed to be deeply involved in learning. I propose a machine learning algorithm that finds patterns in timed data and is highly robust to noise and missing data. It can find both coincident relationships, where two events tend to happen together; as well as causal relationships, where one event appears to be caused by another. I analyze stock price information using this algorithm and strong relationships are found between companies within the same industry. In particular, I worked with 12 stocks taken from the banking, information technology, healthcare, and oil industries. The relationships are almost exclusively coincidental, rather than causal.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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