SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

arXiv:2607.20587 · stat.ML, cs.LG · Submitted 2026-07-22 · Read on arXiv

Hang Ye, Xinyan Jiang, Yuedong Shi, Yangxin Zhu, Jianming Wei, Tian Zheng, Xiaoying Zheng, Yongxin Zhu

stat.ML, cs.LG

Submitted: 2026-07-22

Comments: 10 pages, 4 figures, 3 tables. Submitted to IEEE Transactions on Power Systems

Code: https://github.com/yedadasd/SPECTRA

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

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Decoupling — 9× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 8× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Transformers — 5× in this paper · explained in 2 episodes
• Time Series Forecasting — 4× in this paper · explained in 4 episodes
• Calibration — 3× in this paper · explained in 9 episodes
• Core — 3× in this paper · explained in 2 episodes
• Exogenous Context Synergizer — 3× in this paper
• Wavelet Transform — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Robustness — 2× in this paper · explained in 6 episodes
• Ablation Study — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 10 episodes
• Distribution Shift — 1× in this paper · explained in 12 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Hybrid Model — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Residual Connections — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Training Objective — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 6 episodes

Sources

• Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects
• Efficiently Modeling Long Sequences with Structured State Spaces

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