{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T05:15:28Z","timestamp":1733375728257,"version":"3.30.1"},"reference-count":57,"publisher":"Vilnius University Press","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.15388\/24-infor572","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T06:49:58Z","timestamp":1727765398000},"page":"687-719","source":"Crossref","is-referenced-by-count":0,"title":["On the Improvements of Metaheuristic Optimization-Based Strategies for Time Series Structural Break Detection"],"prefix":"10.15388","author":[{"given":"Mateusz","family":"Burczaniuk","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5361-5787","authenticated-orcid":false,"given":"Agnieszka","family":"Jastrz\u0119bska","sequence":"additional","affiliation":[]}],"member":"6097","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"2024120408521422363_j_infor572_ref_001","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/B978-0-12-813314-9.00010-4","volume-title":"Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications","year":"2018"},{"key":"2024120408521422363_j_infor572_ref_002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00181-021-02137-w","article-title":"Using structural break inference for forecasting time series","volume":"63","year":"2022","journal-title":"Empirical Economics"},{"issue":"1","key":"2024120408521422363_j_infor572_ref_003","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2307\/2998540","article-title":"Estimating and testing linear models with multiple structural changes","volume":"66","year":"1998","journal-title":"Econometrica"},{"issue":"1","key":"2024120408521422363_j_infor572_ref_004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/jae.659","article-title":"Computation and analysis of multiple structural change models","volume":"18","year":"2003","journal-title":"Journal of Applied Econometrics"},{"issue":"2","key":"2024120408521422363_j_infor572_ref_005","article-title":"The likelihood ratio test for structural changes in factor models","volume":"238","year":"2024","journal-title":"Journal of Econometrics"},{"issue":"1","key":"2024120408521422363_j_infor572_ref_006","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1080\/00401706.2018.1438926","article-title":"Most recent changepoint detection in panel data","volume":"61","year":"2019","journal-title":"Technometrics"},{"issue":"11","key":"2024120408521422363_j_infor572_ref_007","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1080\/14697688.2021.1914855","article-title":"Structural breaks in Box-Cox transforms of realized volatility: a model selection perspective","volume":"21","year":"2021","journal-title":"Quantitative Finance"},{"issue":"1","key":"2024120408521422363_j_infor572_ref_008","doi-asserted-by":"crossref","first-page":"19","DOI":"10.21638\/11701\/spbu10.2020.102","article-title":"On the practical applicability of three cusum-methods for structural breaks detection in EGARCH-models","volume":"16","year":"2020","journal-title":"Vestnik of Saint Petersburg University. 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