10.5061/DRYAD.VQ83BK3R9
Neuman, Yair
0000-0002-9062-3580
Ben-Gurion University of the Negev
Neuman, Yair
Ben-Gurion University of the Negev
Cohen, Yohai
Gilasio Coding (Israel)
Tamir, Boaz
Ben-Gurion University of the Negev
Short-term prediction through ordinal patterns
Dryad
dataset
2020
natural computation
order patterns
irreversibility
Interdisciplinary research
2021-01-20T00:00:00Z
2021-01-20T00:00:00Z
en
1282354 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Prediction in natural environments is a challenging task, and there is a
lack of clarity around how a myopic organism can make short-term
predictions given limited data availability and cognitive resources. In
this context, we may ask what kind of resources are available to the
organism to help it address the challenge of short-term prediction within
its own cognitive limits. We point to one potentially important resource:
ordinal patterns, which are extensively used in physics but not in the
study of cognitive processes. We explain the potential importance of
ordinal patterns for short-term prediction, and how natural constraints
imposed through (1) ordinal pattern types, (2) their transition
probabilities and (3) their irreversibility signature may support
short-term prediction. Having tested these ideas on a massive data set of
Bitcoin prices representing a highly fluctuating environment, we provide
preliminary empirical support showing how organisms characterized by
bounded rationality may generate short-term predictions by relying on
ordinal patterns.
The data file holdsĀ 60000 samples of 62 minutes of trade prices in
permutations form of the bitcoin exchange bitstamp The readme files
contain the explanation of the code for the article.
The main function has the code for the run and only the local path for the
files directory is needed to be changed. The epoch variable is the window
size to use and can be up to 60.