On the Improvements of Metaheuristic Optimization-Based Strategies for Time Series Structural Break Detection

Burczaniuk, Mateusz; Jastrzębska, Agnieszka

doi:10.15388/24-INFOR572

Informatica

On the Improvements of Metaheuristic Optimization-Based Strategies for Time Series Structural Break Detection

Volume 35, Issue 4 (2024), pp. 687–719

Mateusz Burczaniuk Agnieszka Jastrzębska

https://doi.org/10.15388/24-INFOR572

Pub. online: 1 October 2024 Type: Research Article

Open Access

Received
1 October 2023

Accepted
1 September 2024

Published
1 October 2024

Abstract

Structural break detection is an important time series analysis task. It can be treated as a multi-objective optimization problem, in which we ought to find a time series segmentation such that time series theoretical models constructed on each segment are well-fitted and the segments are long enough to bear meaningful information. Metaheuristic optimization can help us solve this problem. This paper introduces a suite of new cost functions for the structural break detection task. We demonstrate that the new cost functions allow for achieving quantitatively better precision than the cost functions employed in the literature of this domain. We show particular advantages of each new cost function. Furthermore, the paper promotes the use of Particle Swarm Optimization (PSO) in the domain of structural break detection, which so far has relied on the Genetic Algorithm (GA). Our experiments show that PSO outperforms GA for many analysed time series examples. Last but not least, we introduce a non-trivial generalization of the top-performing state-of-the-art approach to the structural break detection problem based on the Minimum Description Length (MDL) rule with autoregressive (AR) model to MDL ARIMA (autoregressive integrated moving average) model.

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Biographies

Burczaniuk Mateusz

Mat.Burczaniuk@gmail.com

M. Burczaniuk received the BSc degree in computer science and the MSc degree in computer science and information systems from the Warsaw University of Technology, Warsaw, Poland, in 2020 and 2021, respectively. His research interests include machine learning, evolutionary computation and swarm intelligence.

Jastrzębska Agnieszka

https://orcid.org/0000-0001-5361-5787

A.Jastrzebska@mini.pw.edu.pl

A. Jastrzębska received the BSc degree in information technology from the University of Derby, Derby, UK, in 2009, the MScEng degree in computer engineering from the Rzeszow University of Technology, Rzeszow, Poland, in 2010, the MA degree in economics from the University of Rzeszow, Rzeszow, in 2011, and the PhD and DSc degrees from the Warsaw University of Technology, Warsaw, Poland, in 2016 and 2021, respectively. She is associate professor with the Faculty of Mathematics and Information Science, Warsaw University of Technology. Her research interests include machine learning and fuzzy modeling. She serves as an associate editor of Applied Soft Computing journal.

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Keywords

time series structural break ARIMA Genetic Algorithm Particle Swarm Optimization Ant Colony Optimization Minimum Description Length

Funding

This research was supported by the Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) programme.

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