论文标题
预测分层时间序列
Forecasting Hierarchical Time Series
论文作者
论文摘要
本文解决了分层时间序列的常见问题。时间序列分析要求该模型是相应子级别的多个序列的总和。分层时间序列提出了两个问题。首先，必须单独估计层次结构中每个级别的每个单个时间序列模型。其次，这些模型必须在指定的时间段内保持其层次结构，这会因层次结构中高级模型的性能下降而复杂化。这种性能损失归因于底层时间序列模型的总和。在本文中，提出的方法可以通过使用赔率，时间序列和线性方程系统来纠正这种绩效的降级。垂直方面，在计算水平赔率时捕获每个子级别的相应序列的总数，以建立和保留每个级别的每个相应时间序列模型之间的关系。结果基于均方根百分比误差，并具有模拟的层次时间序列数据，这是有希望的。
This paper addresses a common problem with hierarchical time series. Time series analysis demands the series for a model to be the sum of multiple series at corresponding sub-levels. Hierarchical Time Series presents a two-fold problem. First, each individual time series model at each level in the hierarchy must be estimated separately. Second, those models must maintain their hierarchical structure over the specified period of time, which is complicated by performance degradation of the higher-level models in the hierarchy. This performance loss is attributable to the summation of the bottom-level time series models. In this paper, the proposed methodology works to correct this degradation of performance through a top-down approach using odds, time series and systems of linear equations. Vertically, the total counts of corresponding series at each sub-level are captured while horizontally odds are computed to establish and preserve the relationship between each respective time series model at each level. The results, based on root mean square percentage error with simulated hierarchical time series data, are promising.
