  The recurrent thread of dengue incidence in Sri Lanka is still abundant and
it creates a huge burden to the country. Hence, the National Dengue Control
Unit of Sri Lanka propose a national action plan to prevent and control the
dengue incidence. To implement the necessary actions for short and long terms
the proposed plan operates under three levels:country-level, province-level,
and district levels. In order to optimize resource allocation, the health
officers require the forecasts for country, province and district levels, which
preserves the aggregate consistency associated with the district, province, and
country levels as well as time correlations. Hence, the objective of this study
is to forecast the dengue incidence in Sri Lanka using a hierarchical time
series forecasting approach based on spatial and temporal hierarchical
structures. Hierarchical forecasting involves two steps such as generating base
forecasts and reconciliation of these base forecasts. In this study,
Exponential smoothing(ETS) and Autoregressive Integrated Moving Average
(ARIMA), NAIVE, Seasonal NAIVEapproaches and average method are used to
generate base forecasts. Accuracy of forecasts is evaluated using Mean Absolute
Scale Error (MASE). We compare the accuracy of hierarchical time series
forecasts with other benchmark approaches. The forecast accuracy reveals that
the best forecasting approaches for country, provinces and districts are not
limited to a single approach. Hence, we investigate reasons for the variations
of performance in different forecasting approaches based on a time series
feature-based visualization approach.
