SpikeLite：轻量级脉冲神经网络框架用于时间序列预测
SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting
一篇把 SNN 做时间序列预测的论文，用 FSSE 和 SSCA 两个模块在 12 个基准上拿了最好成绩，能耗还是已报道最低，做时序预测的可以看看。
SpikeLite 是一个基于脉冲神经网络（SNN）的时间序列预测框架，包含两个模块：频率选择脉冲编码器（FSSE）利用 LIF 动力学的低通滤波特性分解输入序列，稀疏脉冲通道注意力（SSCA）通过二值掩码筛选跨通道信息交互。在 SeqSNN 和 SpikF 两个协议下，覆盖 4 个多变量和 8 个长期预测基准，SpikeLite 取得最佳综合成绩，平均 R² 为 0.790、RSE 为 0.440，长期预测平均 MSE/MAE 为 0.343/0.345。在 ECL 数据集上的评测显示其能耗为已报道结果中最低。当通道间无需显式交互时，框架可退化为仅用 FSSE 的通道独立路径以进一步降低开销。
SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting
Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN forecasters often pursue higher accuracy through increasingly complex attention mechanisms, or specialized neuronal dynamics, weakening the lightweight motivation of SNNs. We introduce SpikeLite, a spiking forecasting framework built around two modules: a Frequency-Selective Spiking Encoder (FSSE) for frequency-sensitive temporal encoding and a Sparse Spiking Channel Attention (SSCA) module for selective cross-channel interaction. FSSE exploits the low-pass filtering behavior of LIF dynamics to reorganize each input sequence into frequency-sensitive components while collectively preserving the input at the decomposition stage. SSCA then learns a binary mask from encoded channel representations and uses it to selectively exchange information within spike-driven self-attention, retaining informative cross-channel interactions while suppressing redundant ones. When explicit channel interaction is unnecessary, SpikeLite uses the lighter FSSE-only channel-independent path. Experiments under the SeqSNN and SpikF protocols cover four standard multivariate and eight long-term forecasting benchmarks. SpikeLite achieves the best aggregate performance under both protocols, with an average $R^2$ of 0.790 and RSE of 0.440, and lowest average MSE/MAE of 0.343/0.345 in long-term forecasting. Moreover, evaluation on the ECL dataset shows that SpikeLite achieves the lowest reported energy consumption, further demonstrating its potential for energy-efficient time-series forecasting.
