标题： 时间序列情景预测 显示英文标题

标题： Time-series Scenario Forecasting

摘要： 许多应用需要判断时间序列预测的不确定性。 不确定性通常被指定为围绕均值或中位数预测的逐点误差棒。 由于时间依赖性，这种方法会隐藏一些信息。 我们理想上需要一种方法，可以查询给定预测变量的时间序列的后验概率，或者至少能够从此分布中抽取样本。 我们使用贝叶斯字典学习算法来统计生成预测的集合。 我们表明，该算法在休斯顿温度预测方面表现与基于物理的集合方法相当。 我们得出结论，该方法在缺乏基于物理的方法的情景预测中显示出前景。 显示英文摘要

摘要： Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior probability of the entire time-series given the predictive variables, or at a minimum, be able to draw samples from this distribution. We use a Bayesian dictionary learning algorithm to statistically generate an ensemble of forecasts. We show that the algorithm performs as well as a physics-based ensemble method for temperature forecasts for Houston. We conclude that the method shows promise for scenario forecasting where physics-based methods are absent.