论文标题
使用结构化正则化及其应用于人工神经网络的分层时间序列的预测
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks
论文作者
论文摘要
本文讨论了分层时间序列的预测，其中每个高级时间序列都是通过求和适当的下层时间序列来计算的。此类分层时间序列的预测应该是连贯的，这意味着高级时间序列的预测等于相应的下层时间序列的预测之和。以前的进行相干预测的方法由两个阶段组成：第一个计算基础（不一致）的预测，然后根据其固有的层次结构对这些预测进行核对。为了改善时间序列预测，我们提出了一种结构化正则化方法，以同时完成两个阶段。所提出的方法基于底级时间序列的预测模型，并使用结构化正则化项将高级预测纳入预测模型。我们还开发了一种专门用于将我们方法应用于时间序列预测的人工神经网络的反向传播算法。使用合成和现实世界数据集的实验结果证明了我们方法在预测准确性和计算效率方面的优越性。
This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time series. Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure. With the aim of improving time series predictions, we propose a structured regularization method for completing both phases simultaneously. The proposed method is based on a prediction model for bottom-level time series and uses a structured regularization term to incorporate upper-level forecasts into the prediction model. We also develop a backpropagation algorithm specialized for application of our method to artificial neural networks for time series prediction. Experimental results using synthetic and real-world datasets demonstrate the superiority of our method in terms of prediction accuracy and computational efficiency.
