Stock price prediction based on wavelet transform and L2-LSTM

TANG Xuan; LI Lu

doi:10.12299/jsues.24-0039

Volume 39 Issue 1

May 2025

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Article Navigation > Journal of Shanghai University of Engineering Science > 2025 > 39(1): 86-92

TANG Xuan, LI Lu. Stock price prediction based on wavelet transform and L2-LSTM[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 86-92. doi: 10.12299/jsues.24-0039

Citation:

TANG Xuan, LI Lu. Stock price prediction based on wavelet transform and L2-LSTM[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 86-92. doi: 10.12299/jsues.24-0039

TANG Xuan, LI Lu. Stock price prediction based on wavelet transform and L2-LSTM[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 86-92. doi: 10.12299/jsues.24-0039

Citation:

TANG Xuan, LI Lu. Stock price prediction based on wavelet transform and L2-LSTM[J]. Journal of Shanghai University of Engineering Science, 2025, 39(1): 86-92. doi: 10.12299/jsues.24-0039

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Stock price prediction based on wavelet transform and L2-LSTM

doi: 10.12299/jsues.24-0039

TANG Xuan,
LI Lu^,

School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-02-28
Publish Date: 2025-05-19

Abstract

Abstract

Long short-term memory (LSTM) networks are widely used for stock price prediction; however, their performance tends to be suboptimal when applied to stock price data with significant fluctuations. A novel WT-L2-LSTM model was proposed, which integrates wavelet transform with L2-LSTM. The original broadband data was decomposed into multiple layers of narrowband data using wavelet transform, and then the L2-LSTM model was used for the prediction layer by layer. The decomposed data is smoother and allows for parameter tuning layer by layer. The prediction results demonstrate that the WT-L2-LSTM model is superior to the LSTM model in terms of goodness of fit and mean square error, indicating that the WT-L2-LSTM model has better accuracy and generalization performance in predicting stock prices.
- stock price prediction,
- wavelet transform,
- long short-term memory (LSTM),
- L2 regularization

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References(20)

References

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