Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. Recent state-of-art probabilistic forecasting methods also impose hierarchical relations on point predictions and samples of distribution which does not account for coherency of forecast distributions. Previous works also silently assume that datasets are always consistent with given hierarchical relations and do not adapt to real-world datasets that show deviation from this assumption. We close both these gaps and propose PROFHIT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy. PROFHIT uses a flexible probabilistic Bayesian approach and introduces a novel Distributional Coherency regularization to learn from hierarchical relations for entire forecast distribution that enables robust and calibrated forecasts as well as adapt to datasets of varying hierarchical consistency. On evaluating PROFHIT over wide range of datasets, we observed 41-88% better performance in accuracy and calibration. Due to modeling the coherency over full distribution, we observed that PROFHIT can robustly provide reliable forecasts even if up to 10% of input time-series data is missing where other methods' performance severely degrade by over 70%.
翻译:概率性的时间序列预测是时间序列预测的一个重要变体,其目标在于建模和预测具有等级关系的多变时间序列。 多数方法侧重于点预测,不提供准确的概率性预测分布。 最新最先进的概率性预测方法也使点预测和分布样本的等级关系与预测分布的一致性无关。 先前的工作还默默地假设数据集总是与给定的等级关系一致,并且不适应显示偏离这一假设的真实世界数据集。 我们缩小了这两个差距,并提出了PROFHIT,这是一个完全概率性的等级预测模型,共同模型预测整个等级的分布。 PROFHIT采用灵活的概率性能比比比比比比方方法,引入新的分布性能调节,以学习整个预测分布的等级关系,从而使得能够进行稳健和校准的预测,并适应不同等级一致性的数据集。 在对广泛的数据集进行评价时,我们观察到了41-88F的准确性级预测模式, 也就是通过精确性和校准性能的70比方方法,我们观察到了比方的准确性和精确性预测。