Dilated Convolutional Neural Networks for Time Series Forecasting

1 code implementation • Journal of Computational Finance, Forthcoming 2018 • Anastasia Borovykh ∗ Sander Bohte † Cornelis W. Oosterlee

The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series.

Time Series Time Series Forecasting