  Time-series forecasting is one of the most active research topics in
artificial intelligence. Applications in real-world time series should consider
two factors for achieving reliable predictions: modeling dynamic dependencies
among multiple variables and adjusting the model's intrinsic hyperparameters. A
still open gap in that literature is that statistical and ensemble learning
approaches systematically present lower predictive performance than deep
learning methods. They generally disregard the data sequence aspect entangled
with multivariate data represented in more than one time series. Conversely,
this work presents a novel neural network architecture for time-series
forecasting that combines the power of graph evolution with deep recurrent
learning on distinct data distributions; we named our method Recurrent Graph
Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate
relationships between co-occurring time-series by assuming that the temporal
data depends not only on inner variables and intra-temporal relationships
(i.e., observations from itself) but also on outer variables and inter-temporal
relationships (i.e., observations from other-selves). An extensive set of
experiments was conducted comparing ReGENN with dozens of ensemble methods and
classical statistical ones, showing sound improvement of up to 64.87% over the
competing algorithms. Furthermore, we present an analysis of the intermediate
weights arising from ReGENN, showing that by looking at inter and
intra-temporal relationships simultaneously, time-series forecasting is majorly
improved if paying attention to how multiple multivariate data synchronously
evolve.
