Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
CoRR(2024)
摘要
Spiking neural networks (SNNs), inspired by the spiking behavior of
biological neurons, provide a unique pathway for capturing the intricacies of
temporal data. However, applying SNNs to time-series forecasting is challenging
due to difficulties in effective temporal alignment, complexities in encoding
processes, and the absence of standardized guidelines for model selection. In
this paper, we propose a framework for SNNs in time-series forecasting tasks,
leveraging the efficiency of spiking neurons in processing temporal
information. Through a series of experiments, we demonstrate that our proposed
SNN-based approaches achieve comparable or superior results to traditional
time-series forecasting methods on diverse benchmarks with much less energy
consumption. Furthermore, we conduct detailed analysis experiments to assess
the SNN's capacity to capture temporal dependencies within time-series data,
offering valuable insights into its nuanced strengths and effectiveness in
modeling the intricate dynamics of temporal data. Our study contributes to the
expanding field of SNNs and offers a promising alternative for time-series
forecasting tasks, presenting a pathway for the development of more
biologically inspired and temporally aware forecasting models.
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