SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2106.09305v3)

Abstract: 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. By combining these rich features aggregated from multiple resolutions, SCINet effectively models time series with complex temporal dynamics. Experimental results show that SCINet achieves significant forecasting accuracy improvements over both existing convolutional models and Transformer-based solutions across various real-world time series forecasting datasets. Our codes and data are available at https://github.com/cure-lab/SCINet.

• The paper introduces SCINet’s key contribution: a recursive architecture that captures multi-resolution temporal dynamics.
• It employs SCI-Blocks which downsample inputs and exchange features to mitigate information loss during convolution.
• Empirical results show over 40% improvement in mean squared error, outperforming conventional convolutional and Transformer models.

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

The paper introduces SCINet, a novel neural network architecture designed for time series forecasting. SCINet leverages the intrinsic nature of time series data, where temporal relations are preserved even after downsampling. The architecture utilizes a recursive downsample-convolve-interact approach to effectively capture complex temporal dynamics across various resolutions.

Core Contributions

• SCINet Architecture: The architecture is built upon a recursive structure that employs convolutional filters to extract and interact temporal features from downsampled sub-sequences. This method allows the model to integrate information from multiple time resolutions, catering to diverse temporal dynamics within the data.
• SCI-Block Design: SCINet comprises modular components called SCI-Blocks. Each block downsamples the input into two sub-sequences, processes them with distinct convolutional filters, and then uses an interactive mechanism to exchange information between the two. This design serves to mitigate the information loss inherent in the downsampling process.

Experimental Insights

Empirical results demonstrate SCINet’s superior performance over existing models, including both convolutional architectures and Transformer-based approaches, across a wide range of time series datasets. On average, SCINet shows a significant improvement in forecasting accuracy, with results indicating more than a 40% relative improvement in terms of mean squared error in certain benchmarks.

Theoretical and Practical Implications

Theoretically, SCINet challenges the conventional reliance on shared convolutional filters in temporal convolutional networks (TCNs), which tend to offer limited feature extraction capabilities. The hierarchical structure of SCINet, characterized by multiple temporal resolutions and interactive learning, provides a broader and richer framework for capturing complex temporal patterns.

Practically, SCINet’s model efficiency, characterized by its computational cost being on par with traditional TCN architectures, makes it well-suited for deployment in real-world scenarios where large-scale time series datasets are common. Its performance in spatial-temporal forecasting tasks suggests potential applications in fields like traffic flow prediction and energy consumption modeling.

Future Developments

Looking ahead, SCINet's architecture could inspire further innovations in handling irregular or sparse time series data due to its inherent robustness against downsampling. Additionally, the integration of explicit spatial modeling techniques within SCINet could bolster its application in scenarios requiring spatial-temporal analyses. The transition towards probabilistic forecasting models could also leverage the SCINet architecture to provide uncertainty estimates, thus enhancing decision-making in critical applications.

In conclusion, SCINet represents a notable advancement in time series forecasting methodologies, offering robust performance and nuanced temporal insights that have broad implications across various scientific and engineering domains.