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Research on trend analysis method of multi-series economic data based on correlation enhancement of deep learning

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Abstract

The analysis on economic data based on time series takes an important position in the field of analysis on time-series data and is also an important task of the field of big data and artificial intelligence. Traditional time-series analysis method is of relatively weak competence in dealing with multi-series analysis. In this research, based on the problem associated with the analysis on time-series economic data, efficient handling method and model are put forward in the face of multi-series analysis task. Also, combined with the association rules, trend correlation and self-trend correlation among multiple series, a trend and correlation deep neural network model (TC-DNM) is established and then tested and verified by using three kinds of economic datasets with representativeness based on the trend analysis task handed by multi-series analysis. The results show that the model proposed in this research is effective than a number of baseline models, can be employed to achieve precision–recall balance and also possesses strong reusability. The two correlation models and joint models in this paper are of peculiarity and innovativeness.

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Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, China

    Weihan Wang & Weiping Li

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  1. Weihan Wang
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  2. Weiping Li
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Correspondence to Weiping Li.

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Wang, W., Li, W. Research on trend analysis method of multi-series economic data based on correlation enhancement of deep learning. Neural Comput & Applic 33, 4815–4831 (2021). https://doi.org/10.1007/s00521-020-05263-1

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  • DOI: https://doi.org/10.1007/s00521-020-05263-1

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