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Applications of Kalman Filtering in Time Series Prediction

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Intelligent Robotics and Applications (ICIRA 2022)

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Abstract

With the development of big data techniques, various data are accumulated and used for time series prediction. As an optimal estimation algorithm, Kalman filtering (KF) is a useful method in realizing time series prediction for linear systems. In this paper, the characteristics of KF and its derivative algorithms (KFDAs) are analyzed and summarized. The existing application results about KFDAs are reviewed respectively in carrying on time series prediction of wind speed and finance. The available comparison results of KFDAs and neural network models are surveyed and discussed on conducting time series prediction, and it is revealed that KFDAs usually outperform neural network.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U21A2019, 61873058, 62073070, 62103096 and 11902072, the Heilongjiang Postdoctoral Foundation under Grant LBH-Z18045, the Hainan Province Science and Technology Special Fund of China under Grant ZDYF2022SHFZ105, the Fundamental Research Funds for Provincial Undergraduate Universities of Heilongjiang Province of China under Grant 2018QNL-56, the Technology Plan Project of Daqing City of China under Grants zd-2019-17 and zd-2020-26, and the Alexander von Humboldt Foundation of Germany.

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Authors and Affiliations

  1. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China

    Xuegui Li, Shuo Feng, Nan Hou, Hanyang Li & Shuai Zhang

  2. School of Computer and Information Technology, Northeast Petroleum University, Daqing, China

    Xuegui Li, Shuo Feng, Nan Hou, Hanyang Li, Shuai Zhang, Zhen Jian & Qianlong Zi

  3. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, China

    Xuegui Li, Shuo Feng, Nan Hou, Hanyang Li & Shuai Zhang

  4. Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, China

    Xuegui Li & Nan Hou

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  1. Xuegui Li
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  2. Shuo Feng
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  3. Nan Hou
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  4. Hanyang Li
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  7. Qianlong Zi
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Correspondence to Shuo Feng .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Harbin, China

    Weihong Ren

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Li, X. et al. (2022). Applications of Kalman Filtering in Time Series Prediction. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_47

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  • DOI: https://doi.org/10.1007/978-3-031-13835-5_47

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