Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts

• URL: http://arxiv.org/abs/2507.15079v1
• Date: Sun, 20 Jul 2025 18:28:39 GMT
• Title: Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
• Authors: Arkadiusz Lipiecki, Bartosz Uniejewski,
• Abstract summary: We propose a novel method for generating probabilistic forecasts from ensembles of point forecasts, called Isotonic Quantile Regression Averaging (iQRA)<n>We show that iQRA consistently outperforms state-of-the-art postprocessing methods in terms of both reliability and sharpness.<n>It produces well-calibrated prediction intervals across multiple confidence levels, providing superior reliability to all benchmark methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantifying the uncertainty of forecasting models is essential to assess and mitigate the risks associated with data-driven decisions, especially in volatile domains such as electricity markets. Machine learning methods can provide highly accurate electricity price forecasts, critical for informing the decisions of market participants. However, these models often lack uncertainty estimates, which limits the ability of decision makers to avoid unnecessary risks. In this paper, we propose a novel method for generating probabilistic forecasts from ensembles of point forecasts, called Isotonic Quantile Regression Averaging (iQRA). Building on the established framework of Quantile Regression Averaging (QRA), we introduce stochastic order constraints to improve forecast accuracy, reliability, and computational costs. In an extensive forecasting study of the German day-ahead electricity market, we show that iQRA consistently outperforms state-of-the-art postprocessing methods in terms of both reliability and sharpness. It produces well-calibrated prediction intervals across multiple confidence levels, providing superior reliability to all benchmark methods, particularly coverage-based conformal prediction. In addition, isotonic regularization decreases the complexity of the quantile regression problem and offers a hyperparameter-free approach to variable selection.

Related papers

• Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market [0.0]
  integration of renewable energy into electricity markets poses significant challenges to price stability.<n>This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques.<n>We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques.
  arXiv  Detail & Related papers  (2025-02-07T13:57:47Z)
• Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand [8.068451210598678]
  Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts. We propose a novel general approach for distributional forecasting capable of predicting arbitrary quantiles.
  arXiv  Detail & Related papers  (2024-04-26T14:43:19Z)
• RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction [4.059196561157555]
  Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making. We propose RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality.
  arXiv  Detail & Related papers  (2024-02-16T15:34:07Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model [22.428737156882708]
  The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty.
  arXiv  Detail & Related papers  (2023-05-31T05:04:50Z)
• Lightweight, Uncertainty-Aware Conformalized Visual Odometry [2.429910016019183]
  Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics. Emerging edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties. This paper presents a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands.
  arXiv  Detail & Related papers  (2023-03-03T20:37:55Z)
• Calibrated Regression Against An Adversary Without Regret [10.470326550507117]
  We introduce online algorithms guaranteed to achieve these goals on arbitrary streams of data points. Specifically, our algorithms produce forecasts that are (1) calibrated -- i.e., an 80% confidence interval contains the true outcome 80% of the time. We implement a post-hoc recalibration strategy that provably achieves these goals in regression.
  arXiv  Detail & Related papers  (2023-02-23T17:42:11Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.