Hierarchical forecasting problems arise when time series compose a group structure that naturally defines aggregation and disaggregation coherence constraints for the predictions. In this work, we explore a new forecast representation, the Poisson Mixture Mesh (PMM), that can produce probabilistic, coherent predictions; it is compatible with the neural forecasting innovations, and defines simple aggregation and disaggregation rules capable of accommodating hierarchical structures, unknown during its optimization. We performed an empirical evaluation to compare the PMM \ to other hierarchical forecasting methods on Australian domestic tourism data, where we obtain a 20 percent relative improvement.
翻译:当时间序列构成一个自然界定预测的汇总和分类一致性制约的分组结构时,就会产生等级预测问题。 在这项工作中,我们探索了新的预测说明,即Poisson Mixture Mesh(PMM),它可以产生概率性、一致性的预测;它与神经预测创新相容,并界定能够容纳等级结构的简单汇总和分类规则,在优化期间并不为人所知。我们进行了实证评估,将澳大利亚国内旅游数据中的PMM \ 与其他等级预测方法进行比较,我们取得了20%的相对改善。