SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting

arXiv:2606.13901 · cs.LG, cs.NE · Submitted 2026-06-11 · Read on arXiv

cs.LG, cs.NE

Submitted: 2026-06-11

Updated: 2026-06-11

Comments: 23 pages, 2 figures, 11 tables. Accepted for presentation at ECML PKDD 2026. Code: https://github.com/jafarbakhshaliyev/SpikF-GO

Journal ref: Machine Learning and Knowledge Discovery in Databases. Research Track, ECML PKDD 2026, LNCS 16943, pp. 37-54, Springer (2027)

DOI: 10.1007/978-3-032-37664-0_3

Code: https://github.com/jafarbakhshaliyev/SpikF-GO

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Time Series Forecasting — 19× in this paper · explained in 3 episodes
• Spiking Fourier Graph Operators — 17× in this paper
• Transformer — 13× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Central Pattern Generator — 4× in this paper
• Energy Efficiency — 3× in this paper · explained in 3 episodes
• Mean Absolute Error — 3× in this paper
• Sparsity — 3× in this paper · explained in 4 episodes
• Spiking Neural Networks (SNNs) — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Gaussian Error Linear Unit — 1× in this paper
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Leaky Integrate-and-Fire — 1× in this paper
• Mean Squared Error — 1× in this paper
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
• Training Spiking Neural Networks Using Lessons From Deep Learning
• Spiking Transformer with Spatial-Temporal Attention
• Learning Sparse Neural Networks through $L_0$ Regularization
• Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
• Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
• Efficient Neuromorphic Signal Processing with Loihi 2
• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
• Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
• Attention Spiking Neural Networks
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
• Root Mean Square Layer Normalization
• QKFormer: Hierarchical Spiking Transformer using Q-K Attention
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

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