Learning time series data using cross correlation and its application in bitcoin price prediction

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Zhang, Kang, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Devavrah Shah.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/91884

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/91884