Research Article Open Access

Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series

Saugat Aryal1, Dheynoshan Nadarajah2, Prabath Lakmal Rupasinghe1, Chandimal Jayawardena1 and Dharshana Kasthurirathna1
  • 1 Sri Lanka Institute of Information Technology, Sri Lanka
  • 2 Independent Researcher, Sri Lanka


Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has shown impressive accomplishments across various tasks including sequence learning and time series forecasting. Although most previous studies are focused on applications of deep learning models for single-step ahead prediction, multi-step financial time series forecasting has not been explored exhaustively. This paper aims at extensively evaluating the performance of various state-of-the-art deep learning models for multiple multi-steps ahead prediction horizons on real-world stock and forex markets dataset. Specifically, we focus on Long-Short Term Memory (LSTM) network and its variations, Encoder-Decoder based sequence to sequence models, Temporal Convolution Network (TCN), hybrid Exponential Smoothing- Recurrent Neural Networks (ES-RNN) and Neural Basis Expansion Analysis for interpretable Time Series forecasting (N-BEATS). Experimental results show that the latest deep learning models such as N-BEATS, ES-LSTM and TCN produced better results for all stock market related datasets by obtaining around 50% less Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) scores for each prediction horizon as compared to other models. However, the conventional LSTM-based models still prove to be dominant in the forex domain by comparatively achieving around 2% less error values.

Journal of Computer Science
Volume 16 No. 10, 2020, 1401-1416


Submitted On: 10 August 2020 Published On: 27 October 2020

How to Cite: Aryal, S., Nadarajah, D., Rupasinghe, P. L., Jayawardena, C. & Kasthurirathna, D. (2020). Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series. Journal of Computer Science, 16(10), 1401-1416.

  • 14 Citations



  • Financial Time Series
  • Forecasting
  • Multi-Step Prediction
  • Deep Learning