Author(s)

XiaoXuan Yao

Affiliation(s)

School of Mathematics and Statistics,Guangxi Normal University, Guilin 541006, Guangxi, China.

Corresponding Author

XiaoXuan Yao

ABSTRACT

This study addresses the accuracy limitations of traditional LSTM models in stock price prediction by proposing an innovative hybrid model, the LSTM-KAN model. Combining the classical Long Short-Term Memory (LSTM) network with the Kolmogorov-Arnold Network (KAN), this model aims to enhance the performance of the LSTM model in predicting complex financial time series by leveraging the highly nonlinear expressive power of KAN. Through empirical analysis of historical stock data, a comparative study is conducted to examine the differences between the LSTM-KAN model and the basic LSTM model in terms of prediction error, stability, and generalization capability. The results demonstrate that the LSTM-KAN model significantly reduces prediction errors in most cases, improving prediction accuracy and providing new perspectives and tools for stock market analysis.

KEYWORDS

LSTM; Kolmogorov-Arnold Network; Stock price prediction; Time series analysis; Nonlinear models

CITE THIS PAPER

XiaoXuan Yao. LSTM model enhanced by Kolmogorov-Arnold network: Improving stock price prediction accuracy. Trends in Social Sciences and Humanities Research. 2024, 2(4): 84-89. DOI: 10.61784/tsshr3015.

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