R-DCNN combines dilated CNNs with resampling to match the denoising accuracy of standard AR methods and per-signal DCNNs while using far less computation and a single training pass.
An experimental review on deep learning architectures for time series forecasting
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Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach
R-DCNN combines dilated CNNs with resampling to match the denoising accuracy of standard AR methods and per-signal DCNNs while using far less computation and a single training pass.