Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting

TL;DR

This paper introduces Tensor-Train RNNs (TT-RNNs) for financial forecasting, demonstrating their interpretability and superior performance in handling high-dimensional currency data compared to traditional RNNs.

Contribution

The study explores the use of TT-RNNs for interpretable financial modeling, showing how tensor decomposition enhances interpretability and regularization in RNNs.

Findings

TT-RNNs outperform uncompressed RNNs in currency forecasting.

Tensor decomposition provides interpretability to the model.

Regularization effect improves model robustness.

Abstract

Recurrent Neural Networks (RNNs) represent the de facto standard machine learning tool for sequence modelling, owing to their expressive power and memory. However, when dealing with large dimensional data, the corresponding exponential increase in the number of parameters imposes a computational bottleneck. The necessity to equip RNNs with the ability to deal with the curse of dimensionality, such as through the parameter compression ability inherent to tensors, has led to the development of the Tensor-Train RNN (TT-RNN). Despite achieving promising results in many applications, the full potential of the TT-RNN is yet to be explored in the context of interpretable financial modelling, a notoriously challenging task characterized by multi-modal data with low signal-to-noise ratio. To address this issue, we investigate the potential of TT-RNN in the task of financial forecasting of currencies. We show, through the analysis of TT-factors, that the physical meaning underlying tensor decomposition, enables the TT-RNN model to aid the interpretability of results, thus mitigating the notorious "black-box" issue associated with neural networks. Furthermore, simulation results highlight the regularization power of TT decomposition, demonstrating the superior performance of TT-RNN over its uncompressed RNN counterpart and other tensor forecasting methods.