  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.
