Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts. Second, we benchmark ProbPNN with state-of-the-art benchmarks by comparing the achieved normalised continuous ranked probability score (nCRPS) and normalised Pinball Loss (nPL) on two data sets containing in total more than 1000 time series. The results of the benchmarks show that using statistical forecasting components improves the probabilistic forecast performance and that ProbPNN outperforms other deep learning forecasting methods whilst requiring less computation costs.

翻译：概率预测对于商业发展、交通规划及电网平衡等多种下游应用至关重要。这些概率预测大多针对包含日历驱动周期性的时间序列数据执行。然而，现有概率预测方法并未明确考虑这些周期性。因此，本文提出一种基于深度学习的方法，明确纳入这些日历驱动的周期性。本文的贡献体现在两个方面：第一，我们采用利用日历驱动先验知识的统计方法创建滚动统计量，并将其与神经网络相结合以提供更优的概率预测。第二，我们在包含总计超过1000个时间序列的两个数据集上，通过比较归一化连续排序概率评分（nCRPS）和归一化Pinball损失（nPL），将ProbPNN与最先进的基准方法进行对比。基准测试结果表明，使用统计预测组件可提升概率预测性能，且ProbPNN以更低计算成本优于其他深度学习方法。