Modeling complex human behavior in socio-economic networks
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Authors
Dipple, Stephen Wilson
Issue Date
2019-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Physics
Alternative Title
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
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY