相关Ornstein-Uhlenbeck观测值的隐马尔可夫模型及其在汽油价格预测中的应用

IF 3.8 2区数学 Q1 MATHEMATICS, APPLIED Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-01 Epub Date: 2025-01-27 DOI:10.1016/j.cnsns.2025.108630

Dimitrije Cicmilović

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引用次数: 0

摘要

我们提出了一个由隐马尔可夫模型控制的多元Ornstein-Uhlenbeck观测过程，同时假设观测过程之间存在相关性。采用电磁算法对模型参数进行了最优估计。该模型的适用范围是美国的汽油价格。我们将数据集与上面提到的隐马尔可夫模型的不相关实现进行基准测试，并表明相关观测值的建模选择导致更好的预测。

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Hidden Markov Model for correlated Ornstein–Uhlenbeck observations and application to gasoline prices forecasting

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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来源期刊

Communications in Nonlinear Science and Numerical Simulation MATHEMATICS, APPLIED-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

6.80

自引率

7.70%

发文量

378

审稿时长

78 days

期刊介绍： 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.