  In this work we propose a novel approach to utilize convolutional neural
networks for time series forecasting. The time direction of the sequential data
with spatial dimensions $D=1,2$ is considered democratically as the input of a
spatiotemporal $(D+1)$-dimensional convolutional neural network. Latter then
reduces the data stream from $D +1 \to D$ dimensions followed by an
incriminator cell which uses this information to forecast the subsequent time
step. We empirically compare this strategy to convolutional LSTM's and LSTM's
on their performance on the sequential MNIST and the JSB chorals dataset,
respectively. We conclude that temporally folded convolutional neural networks
(TFC's) may outperform the conventional recurrent strategies.
