Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

• URL: http://arxiv.org/abs/2304.09836v2
• Date: Tue, 6 Jun 2023 15:39:51 GMT
• Title: Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
• Authors: \'Etienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados
• Abstract summary: We provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions.
• Score: 73.33395097728128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expectation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the "region of reliability" of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.

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)
• Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference [0.0]
  We argue that, when evaluating a set of intervals, traditional measures alone are insufficient. The intervals must vary in length, with this variation directly linked to the difficulty of the prediction. We propose the Heteroscedastic Quantile Regression (HQR) model and the Width-Adaptive Conformal Inference ( WACI) method.
  arXiv  Detail & Related papers  (2024-06-21T06:51:13Z)
• 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)
• Proper Scoring Rules for Survival Analysis [0.07614628596146598]
  We investigate extensions of four major strictly proper scoring rules for survival analysis. We prove that these extensions are proper under certain conditions, which arise from the discretization of the estimation of probability distributions.
  arXiv  Detail & Related papers  (2023-05-01T01:50:24Z)
• Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks [0.19573380763700712]
  We use a conditional Invertible Neural Network (cINN) to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions.
  arXiv  Detail & Related papers  (2023-02-03T15:11:39Z)
• 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)
• Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization [5.5643498845134545]
  We use generative neural networks to parametrize distributions on high-dimensional spaces by transforming draws from a latent variable. We train generative networks to minimize a predictive-sequential (or prequential) scoring rule on a recorded temporal sequence of the phenomenon of interest. Our method outperforms state-of-the-art adversarial approaches, especially in probabilistic calibration.
  arXiv  Detail & Related papers  (2021-12-15T15:51:12Z)
• 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)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.