Optimizing financial engineering time indicator using bionics computation algorithm and neural network

Jiajia Lu; Yanqiu Ding; Zheng Li

doi:10.1117/12.3009279

16 October 2023 Optimizing financial engineering time indicator using bionics computation algorithm and neural network

Jiajia Lu, Yanqiu Ding, Zheng Li

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032Y (2023) https://doi.org/10.1117/12.3009279
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

To anticipate financial time series (FTS) more accurately and address issues with non-linearity, non-stationarity, and significant noise in financial data, neural networks and bionic algorithms are used. First, the FTS is mined for data using the Long Short-Term Memory (LSTM) neural network, which analyzes the time series’ features to predict how they will change over time. Second, the attention mechanism is used to enhance the LSTM neural network’s effectiveness in forecasting FTS. The experimental findings demonstrate that the forecasting model built has better outcomes for the Shanghai Stock Index and Shenzhen Stock Index in terms of results of fitting. And the error fluctuation range is modest, when compared to the forecasting impact of the RNN (Recurrent Neural Network) model and the LSTM model. Compared with commonly used forecasting algorithms, the forecasting errors of the designed forecasting model are reduced by 57.3%, 46.3%, 25.3%, and 59.4%, respectively. Therefore, the designed attention mechanism-improved LSTM model can forecast the FTS. The above results provide a reference for establishing a FTS forecasting model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiajia Lu, Yanqiu Ding, and Zheng Li "Optimizing financial engineering time indicator using bionics computation algorithm and neural network", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032Y (16 October 2023); https://doi.org/10.1117/12.3009279

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