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

Understand

One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.

• By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet.
• Specifically, SCINet is a recursive downsample-convolve-interact architecture.
• In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features.