Volume 141, Number 6, March 2023
|Number of page(s)||4|
|Section||Statistical physics and networks|
|Published online||17 March 2023|
Equivalence between time series predictability and Bayes error rate
1 School of computer science, Northwestern Polytechnical University - Xi'an 710129, China
2 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu 611731, China
(b) E-mail: email@example.com (corresponding author)
(c) E-mail: firstname.lastname@example.org
Received: 29 November 2022
Accepted: 6 March 2023
Predictability is an emerging metric that quantifies the highest possible prediction accuracy for a given time series, being widely utilized in assessing known prediction algorithms and characterizing intrinsic regularities in human behaviors. Lately, increasing criticisms aim at the inaccuracy of the estimated predictability, caused by the original entropy-based method. In this paper, we strictly prove that the time series predictability is equivalent to a seemingly unrelated metric called Bayes error rate that explores the lowest error rate unavoidable in classification. This proof bridges two independently developed fields, and thus each can immediately benefit from the other. For example, based on three theoretical models with known and controllable upper bounds of prediction accuracy, we show that the estimation based on Bayes error rate can largely solve the inaccuracy problem of predictability.
© 2023 EPLA
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.