  One of the fundamental problems of using optimization models that use
different time series as data input, is the trade-off between model accuracy
and computational tractability. To overcome the computational intractability of
these full optimization models, the dimension of input data and model size is
commonly reduced through time series aggregation (TSA) methods. However,
traditional TSA methods often apply a one-size-fits-all approach based on the
common belief that the clusters that best approximate the input data also lead
to the aggregated model that best approximates the full model, while the metric
that really matters - the resulting output error in optimization results - is
not well addressed. In this paper, we plan to challenge this belief and show
that output-error based TSA methods with theoretical underpinnings have
unprecedented potential of computational efficiency and accuracy.
