Simon Hirsch, Jonathan Berrisch, Florian Ziel
arXiv 26 Jun 2024 · Statistics — Machine Learning
arXiv:2407.08750 · PDF · DOI · OpenAlex · Extracted main text
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 towards using probabilistic forecasts. 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 is based on a combination of recent developments for the online estimation of LASSO models and 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rigby, Robert A and Stasinopoulos, Mikis D (2005) Generalized additive models for location, scale and shape | 0.971 | 12 | 4 | 92% |
| 2 | Groll, Andreas and Hambuckers, Julien and Kneib, Thomas and Umlauf,… (2019) LASSO-type penalization in the framework of generalized additive models for location, scale and shape | 0.928 | 4 | 3 | 100% |
| 3 | Ziel, Florian and Muniain, P and Stasinopoulos, Mikis D (2021) gamlss. lasso: Extra Lasso-Type Additive Terms for GAMLSS self | 0.843 | 4 | 4 | 75% |
| 4 | Angelosante, Daniele and Bazerque, Juan Andrés and Giannakis, Georgi… (2010) Online adaptive estimation of sparse signals: Where RLS meets the $ _1$-norm | 0.811 | 4 | 2 | 100% |
| 5 | Messner, Jakob W and Pinson, Pierre (2019) Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting | 0.811 | 4 | 2 | 100% |
| 6 | Marcjasz, Grzegorz and Narajewski, Michał and Weron, Rafał and Ziel,… (2023) Distributional neural networks for electricity price forecasting self | 0.778 | 17 | 4 | 47% |
| 7 | Kupiec, Paul H (1995) Techniques for Verifying the Accuracy of Risk Measurement Models | 0.737 | 5 | 3 | 40% |
| 8 | Nowotarski, Jakub and Weron, Rafał (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.737 | 5 | 3 | 40% |
| 9 | Stasinopoulos, Mikis D and Rigby, Robert A (2008) Generalized additive models for location scale and shape (GAMLSS) in R | 0.737 | 3 | 2 | 100% |
| 10 | Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert (2010) Regularization paths for generalized linear models via coordinate descent | 0.693 | 5 | 1 | 100% |
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