Uncertainty quantification in forecasting represents a topic of great importance in statistics, especially when dealing with complex data characterized by non-trivial dependence structure. Pushed by novel works concerning distribution-free prediction, we propose a scalable procedure that outputs closed-form simultaneous prediction bands for multivariate functional response variables in a time series setting, which is able to guarantee performance bounds in terms of unconditional coverage and asymptotic exactness, both under some conditions. After evaluating its performance on synthetic data, the method is used to build multivariate prediction bands for daily demand and offer curves in the Italian gas market. The prediction framework thus obtained allows traders to directly evaluate the impact of their own offers/bids on the market, providing an intriguing tool for the business practice.
翻译:预测中的不确定性量化在统计中是一个非常重要的议题,特别是在处理非三边依赖结构的复杂数据时。在无分销预测新作品的推动下,我们提出了一个可缩放的程序,即产出在时间序列背景下用于多变量功能反应变数的封闭式同步预测带,在某些条件下能够保证在无条件覆盖和无症状准确性方面的性能约束。在对合成数据的性能进行评估后,该方法被用来为意大利天然气市场的日常需求建立多变量预测带和提供曲线。由此获得的预测框架使贸易商能够直接评估自己的报价/投标对市场的影响,为商业实践提供了一种令人感兴趣的工具。