  Time series exploration and mining has many applications across several
industrial and scientific domains. In this paper, we consider the problem of
detecting locally similar pairs and groups, called bundles, over co-evolving
time series. These are pairs or groups of subsequences whose values do not
differ by more than {\epsilon} for at least delta consecutive timestamps, thus
indicating common local patterns and trends. We first present a baseline
algorithm that performs a sweep line scan across all timestamps to identify
matches. Then, we propose a filter-verification technique that only examines
candidate matches at judiciously chosen checkpoints across time. Specifically,
we introduce two block scanning algorithms for discovering local pairs and
bundles respectively, which leverage the potential of checkpoints to
aggressively prune the search space. We experimentally evaluate our methods
against real-world and synthetic datasets, demonstrating a speed-up in
execution time by an order of magnitude over the baseline. This paper has been
published in the 16th International Symposium on Spatial and Temporal Databases
(SSTD19).
