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Time‐Series Forecasting Using SVMD‐LSTM: A Hybrid Approach for Stock Market Prediction

Author

Listed:
  • Sanskar Agarwal
  • Shivam Sharma
  • Kazi Newaj Faisal
  • Rishi Raj Sharma

Abstract

The fluctuations in the stock market serve as an indicator of societal and economic progress. To enhance the forecasting of financial time series data, this paper introduces a novel hybrid model that integrates successive variational mode decomposition (SVMD) with a long short‐term memory (LSTM) network. SVMD is used as a data‐decomposition technique to facilitate the division of complex original series into its decomposed intrinsic mode functions in order to reduce nonstationarity. This approach has been analyzed for both synthetic signals and financial time series data. LSTM is utilized to analyze each signal and take into consideration the influence of historical data in order to enhance prediction accuracy. The final predicted results are derived by reconstructing each predicted time series. To evaluate the model, financial time‐series data from global stock market have been selected for analysis, i.e., the Hong Kong Hang Seng Index (HSI), Stock Exchange Sensitive Index (SENSEX), Standard and Poor’s 500 (S&P500), and North America West Texas Intermediate (WTI). The proposed model has shown improved prediction results based on the performance metrics when compared with single models and other existing hybrid models.

Suggested Citation

  • Sanskar Agarwal & Shivam Sharma & Kazi Newaj Faisal & Rishi Raj Sharma, 2025. "Time‐Series Forecasting Using SVMD‐LSTM: A Hybrid Approach for Stock Market Prediction," Journal of Probability and Statistics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jnljps:v:2025:y:2025:i:1:n:9464938
    DOI: 10.1155/jpas/9464938
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    1. Pham Hoang Vuong & Lam Hung Phu & Tran Hong Nguyen & Le Nhat Duy & Pham The Bao & Tan Dat Trinh, 2025. "A comparative study of deep learning approaches for stock price prediction," Digital Finance, Springer, vol. 7(4), pages 623-651, December.

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