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