  Optimal decision-making in social settings is often based on forecasts from
time series (TS) data. Recently, several approaches using deep neural networks
(DNNs) such as recurrent neural networks (RNNs) have been introduced for TS
forecasting and have shown promising results. However, the applicability of
these approaches is being questioned for TS settings where there is a lack of
quality training data and where the TS to forecast exhibit complex behaviors.
Examples of such settings include financial TS forecasting, where producing
accurate and consistent long-term forecasts is notoriously difficult. In this
work, we investigate whether DNN-based models can be used to forecast these TS
conjointly by learning a joint representation of the series instead of
computing the forecast from the raw time-series representations. To this end,
we make use of the dynamic factor graph (DFG) to build a multivariate
autoregressive model. We investigate a common limitation of RNNs that rely on
the DFG framework and propose a novel variable-length attention-based mechanism
(ACTM) to address it. With ACTM, it is possible to vary the autoregressive
order of a TS model over time and model a larger set of probability
distributions than with previous approaches. Using this mechanism, we propose a
self-supervised DNN architecture for multivariate TS forecasting that learns
and takes advantage of the relationships between them. We test our model on two
datasets covering 19 years of investment fund activities. Our experimental
results show that the proposed approach significantly outperforms typical
DNN-based and statistical models at forecasting the 21-day price trajectory. We
point out how improving forecasting accuracy and knowing which forecaster to
use can improve the excess return of autonomous trading strategies.
