We develop a modern deep convolutional neural network for conditional time
series forecasting based on the recent WaveNet architecture. The proposed
network contains stacks of dilated convolutions that widen the receptive field
of the forecast; multiple convolutional filters are applied in parallel to
separate time series and allow for the fast processing of data and the
exploitation of the correlation structure between the multivariate time series.
The performance of the deep convolutional neural network is analyzed on various
multivariate time series including commodities data and stock indices and
compared to that of the well-known autoregressive model and a fully
convolutional network. We show that our network is able to effectively learn
dependencies between the series without the need of long historical time series
and significantly outperforms the baseline neural forecasting models.