Acta mathematica scientia,Series A ›› 2022, Vol. 42 ›› Issue (3): 881-890.

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Regular Kernel Method for State Space Model

Chao Wang(),Bo Li*(),Lei Wang   

  1. Department of Mathematics and Statistics, China Central Normal University, Wuhan 430071
  • Received:2021-01-19 Online:2022-06-26 Published:2022-05-09
  • Contact: Bo Li;
  • Supported by:
    the NSFC(61877023);the Fundamental Research Funds for the Central Universities(CCNU19TD009);the Hubei Provincial Science and Technology Innovation Base (Platform) Special Project(2020DFH002)


State space models(SSMs) provide a general framework for studying stochastic processes, which has been applied in revealing the true underlying economic processes of an economy, recognizing cellphone signals, detecting the loaction of an airplane on a radar screen, et al. In this paper, we study the Markov state space models by modeling the space transformation with reproducing kernel Hilbert space. Not only the existence and uniqueness of solutions are given, but also the error is estimate in L2 spaces. We applied our method in Visibility prediction at airport in National Post-Graduate Mathematical Contest in Modeling supported by China Academic Degrees & Graduate Education Development Center.

Key words: State Space Model, Function Reconstruction, Kernel Method, Autoregression

CLC Number: 

  • O212.5