A General Approach For Supporting Time Series Matching using Multiple-Warped Distances
Rodica Neamtu, Ramoza Ahsan, Cuong Nguyen, and
3 more authors
In IEEE Transactions on Knowledge and Data Engineering, 2020
Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education.
Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a
plethora of dynamic time warping distances. However, the computational costs of using such elastic distances
result in unacceptable response times. We thus design the first practical solution for the efficient GEN eral EX ploration
of time series leveraging multiple warped distances. GENEX pre-processes time series data in metric point-wise distance spaces,
while providing bounds for the accuracy of corresponding analytics derived in non-metric warped distance spaces.
Our empirical evaluation on 66 benchmark datasets provides a comparative study of the accuracy and response times
of diverse warped distances. We show that GENEX is a versatile yet highly efficient solution for processing
expensive-to-compute warped distances over large datasets, with response times 3 to 5 orders of magnitude faster
than state-of-art systems.