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On the Optimal Selection of Time‐Lag Embedding Dimension for Deep Learning Approaches in Financial Forecasting With Big Data

Author

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  • Mohammadreza Ghadimpour
  • Seyed Babak Ebrahimi
  • Stelios Bekrios
  • Ehsan Bagheri

Abstract

Our expectation of stock price trends is the main factor in making a trading strategy or determining the right time to buy or sell a stock. Predictions on stock market prices are a great challenge due to its complexity and the dependence of prices on various economic and non‐economic factors. Today, technological advances have led to proposing new methods in this field. Deep learning is one of these methods that has received much attention in recent years. In this study, using deep learning methods and, more specifically, long‐short term memory (LSTM) and gated recurrent unit (GRU) networks, we predict the S&P 500 index in three different time frames: daily, weekly, and monthly. We also compare the efficiency of these two methods and examine the effect of choosing different time frames for the input data of these networks. The results indicate that the GRU network outperforms the LSTM network. Also, selecting an appropriate time frame for the input data may improve the network accuracy.

Suggested Citation

  • Mohammadreza Ghadimpour & Seyed Babak Ebrahimi & Stelios Bekrios & Ehsan Bagheri, 2026. "On the Optimal Selection of Time‐Lag Embedding Dimension for Deep Learning Approaches in Financial Forecasting With Big Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 114-121, January.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:1:p:114-121
    DOI: 10.1002/for.70007
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