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One unique property of time series is that the temporal relations are largelypreserved after downsampling into two sub-sequences. By taking advantage ofthis property, we propose a novel neural network architecture that conductssample convolution and interaction for temporal modeling and forecasting, namedSCINet. Specifically, SCINet is a recursive downsample-convolve-interactarchitecture. In each layer, we use multiple convolutional filters to extractdistinct yet valuable temporal features from the downsampled sub-sequences orfeatures. By combining these rich features aggregated from multipleresolutions, SCINet effectively models time series with complex temporaldynamics. Experimental results show that SCINet achieves significantforecasting accuracy improvements over both existing convolutional models andTransformer-based solutions across various real-world time series forecastingdatasets. Our codes and data are available athttps://github.com/cure-lab/SCINet.

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction