A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting
Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing challenges for effective operational decision-making. This study introduces a Causal Graph-Informed Temporal Convolutional Network (CG-TCN), a forecasting architecture that integrates a learned causal graph into a temporal convolutional network via a graph-neural embedding to enhance both forecasting accuracy and interpretability of retail electricity price dynamics. It first applies a multi-resolution decomposition to isolate semiannual, quarterly, and monthly trends from high-frequency fluctuations. A causal graph is then discovered over ...
| Authors | Yufan Ji, Abdollah Shafieezadeh, Noah Dormady |
|---|---|
| Primary category | stat.AP |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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