mlsanityData to 14 Oct 2026

Online Distributional Regression

arXiv 2407.08750 ↗First posted 26 Jun 2024stat.ML cs.LG econ.EMcited by 3

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Abstract (arXiv)

Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain management, weather and meteorology, energy markets, and finance, have pivoted toward probabilistic forecasting. This results in the need not only for accurate learning of the expected value but also for learning the conditional heteroskedasticity and conditional moments. Against this backdrop, we present a methodology for online estimation of regularized, linear distributional models. The proposed algorithm combines recent developments in online estimation of LASSO models with the well-known GAMLSS framework. We provide a case study on day-ahead electricity price forecasting, in which we show the competitive performance of the incremental estimation combined with strongly reduced computational effort. Our algorithms are implemented in a computationally efficient Python package ondil.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 64 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

12 of 64 references.

References · 12

  1. 2015Coordinate Descent Algorithmscited by 219
  2. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  3. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
  4. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
  5. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 78
  6. 2020Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regressioncited by 37
  7. 2023Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Meancited by 18
  8. 2019XGBoostLSS -- An extension of XGBoost to probabilistic forecastingcited by 12
  9. 2021M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyondcited by 11
  10. 2015Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processescited by 7
  11. 2020Probabilistic Load Forecasting Based on Adaptive Online Learningcited by 7
  12. 2024On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity pricescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecastingcited by 2
  2. 2026Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learningcited by 1
  3. 2025From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecastingcited by 0