Correction

This article refers to:

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

Article title: APSO-TA-LSTM: A LONG AND SHORT TERM MEMORY MODEL COMBINING TIME ATTENTION AND ADAPTIVE PARTICLE SWARM OPTIMIZATION FOR STOCK FORECASTING

Authors: Tianyu Hao, Gang Song and Hongwei Du

Journal: International Journal of General Systems

DOI: https://doi.org/10.1080/03081079.2023.2222888

Text in the last paragraphs has been modified on page 2 with inclusion of reference citation, point 3 of the text above the section “Related Work” has been modified. New reference text is included in the reference section.

List of changes done:

1. Updated text of the second paragraph is as follows: “Based on the traditional particle swarm optimization algorithm, we proposed an adaptive particle swarm optimization algorithm in our early work that enhances population diversity and demonstrates excellent convergence performance (Gang et al. 2019).

2. The updated point (3) before the section “Related work” is as follows: We utilized an adaptive particle swarm optimization algorithm to optimize key parameters within the model structure, enhancing the model's ability to represent features in stock data. Furthermore, experimental results on three datasets validated the effectiveness of the approach.

3. The new reference inserted is as follows: Gang, Song, Zhang Yunfeng, Bao Fangxun, and Qin Chao. 2019. “Stock prediction model based on particle swarm optimization LSTM.” Journal of Beijing University of Aeronautics and Astronautics 45 (12): 2533–2542