TY  - JOUR
AU  - Aryal, Saugat 
AU  - Nadarajah, Dheynoshan 
AU  - Rupasinghe, Prabath Lakmal 
AU  - Jayawardena, Chandimal 
AU  - Kasthurirathna, Dharshana 
PY  - 2020
TI  - Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series
JF  - Journal of Computer Science
VL  - 16
IS  - 10
DO  - 10.3844/jcssp.2020.1401.1416
UR  - https://thescipub.com/abstract/jcssp.2020.1401.1416
AB  - 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.