Conditional Time Series Forecasting with Convolutional Neural Networks

Anastasia Borovykh, Sander Bohte, Cornelis W. Oosterlee : 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.