  This paper proposes a simple method to extract from a set of multiple related
time series a compressed representation for each time series based on
statistics for the entire set of all time series. This is achieved by a
hierarchical algorithm that first generates an alphabet of shapelets based on
the segmentation of centroids for clustered data, before labels of these
shapelets are assigned to the segmentation of each single time series via
nearest neighbor search using unconstrained dynamic time warping as distance
measure to deal with non-uniform time series lenghts. Thereby, a sequence of
labels is assigned for each time series. Completion of the last label sequence
permits prediction of individual time series. Proposed method is evaluated on
two global COVID-19 datasets, first, for the number of daily net cases (daily
new infections minus daily recoveries), and, second, for the number of daily
deaths attributed to COVID-19 as of April 27, 2020. The first dataset involves
249 time series for different countries, each of length 96. The second dataset
involves 264 time series, each of length 96. Based on detected anomalies in
available data a decentralized exit strategy from lockdowns is advocated.
