Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing

• URL: http://arxiv.org/abs/2005.13417v1
• Date: Wed, 27 May 2020 15:22:10 GMT
• Title: Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
• Authors: Tim Janke and Florian Steinke
• Abstract summary: We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios. Our ensemble post-processing method outperforms well-established model combination benchmarks. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.

Related papers

• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• 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)
• pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting [10.441994923253596]
  pTSE is a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM) We provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods.
  arXiv  Detail & Related papers  (2023-05-16T07:00:57Z)
• Ensemble weather forecast post-processing with a flexible probabilistic neural network approach [0.0]
  We propose a novel, neural network-based method, which produces forecasts for all locations and lead times jointly. We demonstrate the effectiveness of our method in the context of the EUPPBench benchmark.
  arXiv  Detail & Related papers  (2023-03-29T15:18:00Z)
• Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing [6.288767115532775]
  Fog manufacturing can greatly enhance traditional manufacturing systems through distributed computation Fog units. It is known that the predictive offloading methods highly depend on accurate prediction and uncertainty quantification of runtime performance metrics. We propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics.
  arXiv  Detail & Related papers  (2022-10-10T16:14:08Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Aggregating distribution forecasts from deep ensembles [0.0]
  We study the question of how to aggregate distribution forecasts based on neural network-based approaches. We show that combining forecast distributions can substantially improve the predictive performance. We propose a general quantile aggregation framework for deep ensembles that shows superior performance compared to a linear combination of the forecast densities.
  arXiv  Detail & Related papers  (2022-04-05T15:42:51Z)
• 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)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• Adversarial Attacks on Probabilistic Autoregressive Forecasting Models [7.305979446312823]
  We develop an effective generation of adversarial attacks on neural models that output a sequence of probability distributions rather than a sequence of single values. We demonstrate that our approach can successfully generate attacks with small input perturbations in two challenging tasks.
  arXiv  Detail & Related papers  (2020-03-08T13:08:34Z)

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.