APSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting

Abstract

A new stock forecasting model that combines time attention and adaptive particle swarm optimization with LSTM (APSO-TA-LSTM) is proposed to improve the forecasting ability of neural networks for financial time series. The model uses a two-layer LSTM network to encode stock information within the time window and employs time attention to strategically focus on dependencies among time series features for more accurate feature representations. Additionally, the proposed adaptive particle swarm optimization algorithm is used to pick out the key parameters of the network structure and enhance the overall prediction performance. Finally, the experimental results on three stock datasets validate the innovation and effectiveness of our method, and this work will have a broad application prospect in the study of financial time series.

Keywords:

• Stock forecasting
• time attention
• adaptive particle swarm optimization
• LSTM network

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Correction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/03081079.2024.2303820)

Additional information

Funding

This work is supported by National Natural Science Foundation of China [61972227, 61902217], Shandong Provincial Natural Science Foundation Key Project [ZR2020KF015].