Spiking neural networks (SNN) accurately emulate the neurodynamics of biological neurons, theoretically enhancing the processing capabilities for time series data by using spike trains. Existing research, however, has largely remained in an exploratory stage, focusing primarily on the potential of SNNs in time series forecasting rather than classification. Current research on Time Series Classification (TSC) using SNNs often employs complex, pre-trained encoding schemes, relies on reservoir computing, or depends merely on spiking neurons for extracting time series information without fully integrating spiking neurons into the network architecture. Presently, there is a significant gap in SNN research regarding a universally applicable methodology for TSC that balances low temporal complexity with effective, straightforward implementation, trained from scratch, and robust biological plausibility. This article introduces the Masked Timestep SNN (MT-SNN) architecture to selectively mask low neuronal activity timesteps, thereby addressing the entrenched issue of temporal redundancy in SNNs. In addition, our research validates the effectiveness of the direct encoding strategy in SNNs for TSC and proposes the Temporal Adaptive Integrate-and-Fire (TAIF) neuron model, which improves its temporal dynamics through mechanisms of threshold adjustment and fatigue. Our experiments on the UCR Time Series Classification Archive indicate that our approach achieves performance comparable or superior to traditional machine learning and deep learning methods at ultra-low timesteps, obviating the necessity for specialized encoding schemes, preprocessing, or feature engineering. To the best of our knowledge, our approach has achieved the State-of-The-Art (SoTA) spiking result in univariate TSC tasks while maintaining simplicity and biological plausibility, offering a valuable and comprehensive spiking baseline.
Geng et al. (2026) studied this question.