  In this paper we develop methods to solve two problems related to time series
(TS) analysis using quantum computing: reconstruction and classification. We
formulate the task of reconstructing a given TS from a training set of data as
an unconstrained binary optimization (QUBO) problem, which can be solved by
both quantum annealers and gate-model quantum processors. We accomplish this by
discretizing the TS and converting the reconstruction to a set cover problem,
allowing us to perform a one-versus-all method of reconstruction. Using the
solution to the reconstruction problem, we show how to extend this method to
perform semi-supervised classification of TS data. We present results
indicating our method is competitive with current semi- and unsupervised
classification techniques, but using less data than classical techniques.
