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, which yields the need not only for accurate learning of the expected value but also for learning the conditional heteroskedasticity. Against this backdrop, we present a methodology for online estimation of regularized linear distributional models for conditional heteroskedasticity. 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 adaptive estimation combined with strongly reduced computational effort. Our algorithms are implemented in a computationally efficient Python package.

翻译：大规模流数据在现代机器学习应用中十分常见，并推动了在线学习算法的发展。供应链管理、气象与天气学、能源市场和金融等诸多领域已转向使用概率预测，这不仅需要准确学习期望值，还需要学习条件异方差。在此背景下，我们提出了一种用于条件异方差的正则化线性分布模型在线估计方法。所提算法结合了LASSO模型在线估计的最新进展与著名的GAMLSS框架。我们以日前电价预测为例进行案例研究，结果表明自适应估计在显著降低计算量的同时保持了有竞争力的性能。我们的算法已在一个计算高效的Python软件包中实现。