Title: Channel-Similarity Aware Spike Encoding for Multivariate Time-Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Spiking Neural Network, Spike Encoding, Inter-Channel Similarity

Abstract: Spiking Neural Networks (SNNs) have attracted increasing attention for multivariate time-series forecasting due to their intrinsic energy efficiency and suitability for modeling temporal signals. However, existing SNN-based studies have largely focused on temporal dynamics, restricting their scope to spike encoding and temporal modeling. In contrast, recent advances in artificial neural networks demonstrate that modeling inter-channel similarity can substantially enhance forecasting performance. Despite this, such perspectives remain underexplored in the context of SNNs. In this work, we introduce a method that explicitly models inter-channel similarity through channel clustering and integrates it into temporal spike encoding. Specifically, we employ attention-based clustering to quantify channel similarity as cluster memberships, and leverage the Straight-Through Estimator (STE) to enable both end-to-end optimization and seamless integration into spike-based encoding. We evaluate the proposed approach on six benchmark datasets spanning diverse domains and temporal characteristics, using recurrent-, transformer-, and convolution-based SNN backbones. Experimental results show consistent improvements over baseline SNNs, achieving relative reductions in RRSE ranging from 3.0\% to 6.5\%. These findings highlight the potential of inter-channel similarity modeling as a complementary dimension to temporal dynamics in advancing the forecasting capabilities of SNNs.

TL;DR: We propose a spike-form channel clustering method that injects inter-channel similarity into temporal spike encoding, yielding up to 20.1% forecasting improvement across diverse SNN backbones and datasets.