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.
Deep learning for time series forecasting: Advances and open problems
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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.