Adaptive algorithms for change point detection in financial time series

The detection of change points in chaotic and non-stationary time series presents a critical challenge for numerous practical applications, particularly in fields such as finance, climatology, and engineering. Traditional statistical methods, grounded in stationary models, are often ill-suited to ca...

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Main Authors: Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov
Format: Article
Language:English
Published: AIMS Press 2024-12-01
Series:AIMS Mathematics
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Online Access:https://www.aimspress.com/article/doi/10.3934/math.20241674
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author Alexander Musaev
Dmitry Grigoriev
Maxim Kolosov
author_facet Alexander Musaev
Dmitry Grigoriev
Maxim Kolosov
author_sort Alexander Musaev
collection DOAJ
description The detection of change points in chaotic and non-stationary time series presents a critical challenge for numerous practical applications, particularly in fields such as finance, climatology, and engineering. Traditional statistical methods, grounded in stationary models, are often ill-suited to capture the dynamics of processes governed by stochastic chaos. This paper explores modern approaches to change point detection, focusing on multivariate regression analysis and machine learning techniques. We demonstrate the limitations of conventional models and propose hybrid methods that leverage long-term correlations and metric-based learning to improve detection accuracy. Our study presents comparative analyses of existing early detection techniques and introduces advanced algorithms tailored to non-stationary environments, including online and offline segmentation strategies. By applying these methods to financial market data, particularly in monitoring currency pairs like EUR/USD, we illustrate how dynamic filtering and multiregression analysis can significantly enhance the identification of change points. The results underscore the importance of adapting detection models to the specific characteristics of chaotic data, offering practical solutions for improving decision-making in complex systems. Key findings reveal that while no universal solution exists for detecting change points in chaotic time series, integrating machine learning and multivariate approaches allows for more robust and adaptive forecasting models. The work highlights the potential for future advancements in neural network applications and multi-expert decision systems, further enhancing predictive accuracy in volatile environments.
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spelling doaj-art-a4296da387e0410496bdc7bfd1928ac52025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912352383526310.3934/math.20241674Adaptive algorithms for change point detection in financial time seriesAlexander Musaev0Dmitry Grigoriev1Maxim Kolosov2St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, RussiaCenter of Econometrics and Business Analytics (CEBA), St. Petersburg State University, St. Petersburg, RussiaSaint-Petersburg State Institute of Technology (Technical University), St. Petersburg, RussiaThe detection of change points in chaotic and non-stationary time series presents a critical challenge for numerous practical applications, particularly in fields such as finance, climatology, and engineering. Traditional statistical methods, grounded in stationary models, are often ill-suited to capture the dynamics of processes governed by stochastic chaos. This paper explores modern approaches to change point detection, focusing on multivariate regression analysis and machine learning techniques. We demonstrate the limitations of conventional models and propose hybrid methods that leverage long-term correlations and metric-based learning to improve detection accuracy. Our study presents comparative analyses of existing early detection techniques and introduces advanced algorithms tailored to non-stationary environments, including online and offline segmentation strategies. By applying these methods to financial market data, particularly in monitoring currency pairs like EUR/USD, we illustrate how dynamic filtering and multiregression analysis can significantly enhance the identification of change points. The results underscore the importance of adapting detection models to the specific characteristics of chaotic data, offering practical solutions for improving decision-making in complex systems. Key findings reveal that while no universal solution exists for detecting change points in chaotic time series, integrating machine learning and multivariate approaches allows for more robust and adaptive forecasting models. The work highlights the potential for future advancements in neural network applications and multi-expert decision systems, further enhancing predictive accuracy in volatile environments.https://www.aimspress.com/article/doi/10.3934/math.20241674change point detectionnon-stationary processesfinancial time series forecastingproactive change point detectionchaotic time series analysisonline change point detection
spellingShingle Alexander Musaev
Dmitry Grigoriev
Maxim Kolosov
Adaptive algorithms for change point detection in financial time series
AIMS Mathematics
change point detection
non-stationary processes
financial time series forecasting
proactive change point detection
chaotic time series analysis
online change point detection
title Adaptive algorithms for change point detection in financial time series
title_full Adaptive algorithms for change point detection in financial time series
title_fullStr Adaptive algorithms for change point detection in financial time series
title_full_unstemmed Adaptive algorithms for change point detection in financial time series
title_short Adaptive algorithms for change point detection in financial time series
title_sort adaptive algorithms for change point detection in financial time series
topic change point detection
non-stationary processes
financial time series forecasting
proactive change point detection
chaotic time series analysis
online change point detection
url https://www.aimspress.com/article/doi/10.3934/math.20241674
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AT maximkolosov adaptivealgorithmsforchangepointdetectioninfinancialtimeseries