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.
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)