Online Distributional Regression
This work addresses the need for efficient online probabilistic forecasts in fields like energy markets, though it is incremental as it builds on existing LASSO and GAMLSS methods.
The paper tackles the problem of online probabilistic forecasting by introducing a method for online estimation of regularized linear distributional models, combining LASSO and GAMLSS frameworks, and demonstrates competitive performance with reduced computational effort in electricity price forecasting.
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.