Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)

ArXiv ID: 2508.11372 “View on arXiv”

Authors: Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron

Abstract

We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a state-of-the-art transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice.

Keywords: temporal hierarchy forecasting (THieF), electricity prices, forecast reconciliation, block products, German power market, Energy (Electricity)

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs formal hierarchical reconciliation mathematics (e.g., summing matrices, covariance shrinkage) but is heavily grounded in a comprehensive, multi-year backtest across multiple model architectures, using real market data and detailed error metrics.

  flowchart TD
    A["Research Goal: Improve Day-ahead<br>Electricity Price Prediction"] --> B["Methodology: Temporal Hierarchy<br>Forecasting (THieF) with Reconciliation"]
    B --> C{"Data Inputs"}
    C --> D["German Power Market 2021-2024"]
    C --> E["Hourly Prices, Block &<br>Baseload Products"]
    D & E --> F["Computational Process"]
    F --> G["Model Training:<br>Linear Reg, NN, GBM, Transformer"]
    F --> H["Forecast Reconciliation<br>across Temporal Levels"]
    G & H --> I["Key Findings"]
    I --> J["Accuracy Improved up to 13%<br>at all temporal levels"]
    I --> K["Consistent results across<br>4-year test period & all models"]
    I --> L["Reconciliation cost<br>comparable to hourly prediction"]