Learning time series data using cross correlation and its application in bitcoin price prediction
Name
894501187-MIT.pdf
Description
Full printable version
Size
3.26 MB
Format
Adobe PDF
Checksum (MD5)
b2b23aaf09055bc6173b7e38e1b742d2
Author(s)
Zhang, Kang, M. Eng. Massachusetts Institute of Technology
Advisor(s)
Devavrah Shah.
Alternative Title
Bitcoin price prediction using non-parametric learning method
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
In this work, we developed an quantitative trading algorithm for bitcoin that is shown to be profitable. The algorithm establishes a framework that combines parametric variables and non-parametric variables in a logistical regression model, capturing information in both the static states and the evolution of states. The combination improves the performance of the strategy. In addition, we demonstrated that we can discovery curve similarity of time series using cross correlation and L2 distance. The similarity metrics can be efficiently computed using convolution and can help us learn from the past instance using an ensemble voting scheme.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
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
Terms of Use
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