Modeling complex human behavior in socio-economic networks

Authors
Dipple, Stephen Wilson
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Other Contributors
Korniss, Gyorgy
Szymanski, Boleslaw K.
Meunier, Vincent
Giedt, Joel
Issue Date
2019-08
Keywords
Physics
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
In this way, we are able to restrict the distribution of possible outcomes based on the assumption that correlations will persist during our prediction window. Our predictions farther into the future become increasingly inaccurate, however our method is reliable in predicting spikes and dips in the data even during long term predictions. This has greater significance for cryptocurrency markets as the exact values of the market prediction is not as important as simply whether the market will increase or decrease. Our approach indeed has achieved an impressive performance compared to the random prediction and our baseline measure for predicting whether a market will go up or down the following day.
Description
August 2019
School of Science
Department
Dept. of Physics, Applied Physics, and Astronomy
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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