Kalman-SSM: Modeling Long-Term Time Series With Kalman Filter Structured State Spaces

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Zheng Zhou;Xu Guo;Yu-Jie Xiong;Chun-Ming Xia
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引用次数: 0

Abstract

In the field of time series forecasting, time series are often considered as linear time-varying systems, which facilitates the analysis and modeling of time series from a structural state perspective. Due to the non-stationary nature and noise interference in real-world data, existing models struggle to predict long-term time series effectively. To address this issue, we propose a novel model that integrates the Kalman filter with a state space model (SSM) approach to enhance the accuracy of long-term time series forecasting. The Kalman filter requires recursive computation, whereas the SSM approach reformulates the Kalman filtering process into a convolutional form, simplifying training and enhancing model efficiency. Our Kalman-SSM model estimates the future state of dynamic systems for forecasting by utilizing a series of time series data containing noise. In real-world datasets, the Kalman-SSM has demonstrated competitive performance and satisfactory efficiency in comparison to state-of-the-art (SOTA) models.
卡尔曼-SSM:用卡尔曼滤波器结构化状态空间为长期时间序列建模
在时间序列预测领域,时间序列通常被视为线性时变系统,这有利于从结构状态的角度对时间序列进行分析和建模。由于现实世界数据的非平稳性和噪声干扰,现有模型难以有效预测长期时间序列。为解决这一问题,我们提出了一种将卡尔曼滤波器与状态空间模型(SSM)方法相结合的新型模型,以提高长期时间序列预测的准确性。卡尔曼滤波需要递归计算,而状态空间模型方法将卡尔曼滤波过程重新组合为卷积形式,从而简化了训练并提高了模型效率。我们的卡尔曼-SSM 模型通过利用一系列含有噪声的时间序列数据来估计动态系统的未来状态,从而进行预测。在实际数据集中,Kalman-SSM 与最先进的(SOTA)模型相比,表现出了极具竞争力的性能和令人满意的效率。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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