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Forecasting Longitudinal Hierarchical Series (309941)

Keywords: Time Series Forecasting, Longitudinal Hierarchical Series, ARIMA, LSTM, Odds

The paper addresses a common problem with longitudinal hierarchical time series. Time series analysis demands the series for a model to be the sum of multiple series at corresponding sub-levels. Longitudinal 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 lower-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 sub-level are captured while horizontally odds are computed to establish and preserve the relationship between their respective time series models at each level. Initial results based on RMSE with simulated hierarchical time series data demonstrates promising results.