Learning time series data using cross correlation and its application in bitcoin price prediction
Author(s)
Zhang, Kang, M. Eng. Massachusetts Institute of Technology
DownloadFull printable version (3.258Mb)
Alternative title
Bitcoin price prediction using non-parametric learning method
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Devavrah Shah.
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2014Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.