Reconstruction of financial time series data based on compressed sensing

Jingjian Si, Xiangyun Gao, Jinsheng Zhou, Xian Xi, Xiaotian Sun and Yiran Zhao

Finance Research Letters, 2022, vol. 47, issue PA

Abstract: Time series data are widely used in financial research; however, data frequency and completeness can greatly affect the research results. Although high-frequency financial time series data can be obtained, some scenarios, such as bank lending data, may lack high frequency. Currently, mainstream data interpolation methods should improve the data reconstruction accuracy. In this study, we improve the compressed sensing method to expand its field of application, specifically for reconstructing financial data. The results show that the data reconstruction based on compressed sensing can effectively improve the reconstruction accuracy.

Keywords: Time series; Compressed sensing; Financial data; Data reconstruction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:47:y:2022:i:pa:s1544612321005626

DOI: 10.1016/j.frl.2021.102625

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