  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.
