arXiv:2606.13901v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics. More recently, SNNs have been applied to time series forecasting (TSF), with methods exploring spiking temporal backbones, spike-compatible positional encodings, Fourier-domain processing, and redesigned neuron dynamics. However, existing SNN forecasting approaches process variables independently, lacking explicit mechanisms for modeling inter-variable dependencies. This
The paper builds on recent advancements in Spiking Neural Networks (SNNs) and their application to time series forecasting, addressing previous limitations in modeling inter-variable dependencies.
This research could lead to more energy-efficient and effective AI models for complex time series forecasting, critical for AI systems with real-world applications.
The explicit incorporation of inter-variable dependencies in SNNs through graph operators represents a significant architectural improvement for forecasting multivariate time series.
- · AI hardware developers
- · Time series data analytics companies
- · Edge AI computing
- · Energy-efficient AI applications
- · Traditional neural network architectures in specific forecasting tasks
- · Energy-intensive AI model developers
Improved accuracy and efficiency in complex multivariate time series predictions for various industries.
Accelerated adoption of SNNs in industrial and scientific applications due to enhanced practical capabilities.
Reduced operational costs and environmental impact of AI systems due to lower energy consumption, contributing to broader AI accessibility.
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Read at arXiv cs.LG