{"title":"Hidden Markov Model for correlated Ornstein–Uhlenbeck observations and application to gasoline prices forecasting","authors":"Dimitrije Cicmilović","doi":"10.1016/j.cnsns.2025.108630","DOIUrl":null,"url":null,"abstract":"<div><div>We propose a multivariate Ornstein–Uhlenbeck observation process governed by a Hidden Markov model, whereas the correlation between the observation processes is assumed. Optimal estimates of the model parameters are obtained by employing EM algorithm. The scope of application of the model are the gasoline prices in the US. We benchmark the dataset against the uncorrelated implementation of the Hidden Markov Model mentioned above and show that the modeling choice of correlated observations leads to better forecasts.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":"143 ","pages":"Article 108630"},"PeriodicalIF":3.8000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Nonlinear Science and Numerical Simulation","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1007570425000413","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
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Abstract
We propose a multivariate Ornstein–Uhlenbeck observation process governed by a Hidden Markov model, whereas the correlation between the observation processes is assumed. Optimal estimates of the model parameters are obtained by employing EM algorithm. The scope of application of the model are the gasoline prices in the US. We benchmark the dataset against the uncorrelated implementation of the Hidden Markov Model mentioned above and show that the modeling choice of correlated observations leads to better forecasts.
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The journal publishes original research findings on experimental observation, mathematical modeling, theoretical analysis and numerical simulation, for more accurate description, better prediction or novel application, of nonlinear phenomena in science and engineering. It offers a venue for researchers to make rapid exchange of ideas and techniques in nonlinear science and complexity.
The submission of manuscripts with cross-disciplinary approaches in nonlinear science and complexity is particularly encouraged.
Topics of interest:
Nonlinear differential or delay equations, Lie group analysis and asymptotic methods, Discontinuous systems, Fractals, Fractional calculus and dynamics, Nonlinear effects in quantum mechanics, Nonlinear stochastic processes, Experimental nonlinear science, Time-series and signal analysis, Computational methods and simulations in nonlinear science and engineering, Control of dynamical systems, Synchronization, Lyapunov analysis, High-dimensional chaos and turbulence, Chaos in Hamiltonian systems, Integrable systems and solitons, Collective behavior in many-body systems, Biological physics and networks, Nonlinear mechanical systems, Complex systems and complexity.
No length limitation for contributions is set, but only concisely written manuscripts are published. Brief papers are published on the basis of Rapid Communications. Discussions of previously published papers are welcome.
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