使用基于自回归Transformer和条件归一化流的协调进行分层时间序列端到端建模
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
AI总结：
提出基于条件归一化流和自回归Transformer的端到端分层时间序列预测模型，无需后处理即可同时完成预测与协调，在四个真实数据集上验证了方法有效性。
摘要：
具有分层结构的多元时间序列预测在现实应用中普遍存在，不仅需要预测层次结构的每一层，还必须协调所有预测以确保一致性，即预测应满足分层聚合约束。此外，不同层级之间的统计特征差异可能很大，非高斯分布和非线性相关性进一步加剧了这一问题。为此，我们提出了一种新颖的端到端分层时间序列预测模型，基于条件归一化流的自回归Transformer协调机制，在表示复杂数据分布的同时协调预测以确保一致性。与其他最先进方法不同，我们同时实现预测与协调，无需任何显式后处理步骤。此外，通过利用深度模型的能力，我们不依赖无偏估计或高斯分布等假设。评估实验在来自不同工业领域的四个真实世界分层数据集（三个公开数据集和来自Alipay数据中心应用服务器的数据集）上进行，初步结果证明了所提方法的有效性。
英文摘要：
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
