A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting

arXiv:2608.26234 · stat.AP, cs.LG · Submitted 2026-08-26 · Read on arXiv

stat.AP, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Accuracy — 34× in this paper · explained in 1 episode
• Interpretability — 24× in this paper · explained in 15 episodes
• Robustness — 13× in this paper · explained in 5 episodes
• Temporal Convolutional Network — 13× in this paper
• Adjacency Matrix — 12× in this paper · explained in 1 episode
• Causal Discovery — 12× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Graph Neural Network — 7× in this paper
• Sparsity — 6× in this paper · explained in 4 episodes
• Causal Inference — 5× in this paper · explained in 4 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Time Series Forecasting — 5× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Explainability — 4× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Long Short-Term Memory — 4× in this paper
• Regularization — 4× in this paper · explained in 3 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Additive Decomposition — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Causal Reasoning — 2× in this paper · explained in 2 episodes
• Causal Temporal Convolutional Network — 2× in this paper
• Ensemble Learning — 2× in this paper · explained in 2 episodes
• Graph Neural Network (GNN) — 2× in this paper · explained in 3 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 2× in this paper · explained in 3 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Causal Graph-Informed Temporal Convolutional Network — 1× in this paper
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Energy Efficiency — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Multivariate Time Series Graph Neural Network — 1× in this paper
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Public Utilities Commission of Ohio — 1× in this paper
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Semi-Supervised Classification with Graph Convolutional Networks
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
• Adam: A Method for Stochastic Optimization

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