On Partitioning Multivariate Self-Affine Time Series

Taylor, Christopher Michael ORCID: 0000-0002-9844-1001 and Salhi, Abdellah (2017) On Partitioning Multivariate Self-Affine Time Series. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 21 (6). pp. 845-862.

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Official URL: http://dx.doi.org/10.1109/tevc.2017.2688521

Abstract

Given a multivariate time series, possibly of high dimension, with unknown and time-varying joint distribution, it is of interest to be able to completely partition the time series into disjoint, contiguous subseries, each of which has different distributional or pattern attributes from the preceding and succeeding subseries. An additional feature of many time series is that they display self-affinity, so that subseries at one time scale are similar to subseries at another after application of an affine transformation. Such qualities are observed in time series from many disciplines, including biology, medicine, economics, finance, and computer science. This paper defines the relevant multiobjective combinatorial optimization problem with limited assumptions as a biobjective one, and a specialized evolutionary algorithm is presented which finds optimal self-affine time series partitionings with a minimum of choice parameters. The algorithm not only finds partitionings for all possible numbers of partitions given data constraints, but also for self-affinities between these partitionings and some fine-grained partitioning. The resulting set of Pareto-efficient solution sets provides a rich representation of the self-affine properties of a multivariate time series at different locations and time scales.

Item Type:	Article
Uncontrolled Keywords:	Finance, fractals, genetic algorithms, optimization methods, Pareto optimization, partitioning algorithms, time series analysis
Depositing User:	Symplectic Admin
Date Deposited:	14 Oct 2022 09:55
Last Modified:	17 Mar 2024 15:29
DOI:	10.1109/TEVC.2017.2688521
Open Access URL:	https://ieeexplore.ieee.org/abstract/document/7908...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3165472