Conditional Time Series Forecasting with Convolutional Neural Networks

14 Mar 2017Anastasia BorovykhSander BohteCornelis W. Oosterlee

We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. 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... (read more)

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