Abstract:
Finance, medicine, and education generate time series at an unprecedented rate. Exploring heterogeneous, variable-length, and misaligned time series requires many dynamic time warping distances.
The computational costs of elastic distances cause unacceptable response times. We design the first practical solution for efficient general exploration of time series using multiple warped distances. GENEX pre-processes time series data in metric point-wise distance spaces and provides accuracy bounds for non-metric warped distance space analytics.
We compared warped distance accuracy and response times on 66 benchmark datasets. GENEX can process expensive-to-compute warped distances over large datasets with response times 3 to 5 orders of magnitude faster than state-of-the-art systems.
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