Proceedings of the 2021 International Conference on Social Sciences and Big Data Application (ICSSBDA 2021)

Optimal Modelling and Estimation of a Class of Dynamic Signals in Big Data System Analysis

Authors
Xiaonan Xiao
Xiamen University Tan Kah Kee College, Zhangzhou, Fujian, China
*Corresponding author. Email: xiaoxn@xujc.com
Corresponding Author
Xiaonan Xiao
Available Online 17 December 2021.
DOI
https://doi.org/10.2991/assehr.k.211216.048How to use a DOI?
Keywords
Big data system analysis; ynamic signal; Mplex stochastic process; Telligent computing; timization modeling
Abstract

Taking the time series analysis of complex stochastic processes as the main line, this paper makes an in-depth study on a kind of random dynamic information flow widely existing in science and technology, and obtains the nonlinear dynamic model and its optimal control and prediction method under the random century series. In order to further study earthquake prediction, meteorological and financial data analysis, incomplete observation of time series, and outlier processing provide an effective mathematical processing means and method.

Copyright
© 2021 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2021 International Conference on Social Sciences and Big Data Application (ICSSBDA 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
17 December 2021
ISBN
978-94-6239-487-2
ISSN
2352-5398
DOI
https://doi.org/10.2991/assehr.k.211216.048How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaonan Xiao
PY  - 2021
DA  - 2021/12/17
TI  - Optimal Modelling and Estimation of a Class of Dynamic Signals in Big Data System Analysis
BT  - Proceedings of the 2021 International Conference on Social Sciences and Big Data Application (ICSSBDA 2021)
PB  - Atlantis Press
SP  - 261
EP  - 265
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.211216.048
DO  - https://doi.org/10.2991/assehr.k.211216.048
ID  - Xiao2021
ER  -