Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Aggregation and comparison of trajectories
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Minimum distance queries for time series data
Journal of Systems and Software
Information Systems - Databases: Creation, management and utilization
Bounded similarity querying for time-series data
Information and Computation - Special issue: Commemorating the 50th birthday anniversary of Paris C. Kanellakis
Searching for similar trajectories in spatial networks
Journal of Systems and Software
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Time series data is usually stored and processed in the form of discrete trajectories of multidimensional measurement points. In order to compare the measurements of a query trajectory to a set of stored trajectories, one needs to calculate similarity between two trajectories. In this paper an efficient algorithm for calculating the similarity is presented for a set of trajectories containing one increasing measurement dimension, for example time series data. An even more efficient version of the algorithm suitable for situations where all dimensions are increasing, such as many spatio-temporal data sets, is also presented. Furthermore, the similarity measurement technique nearly fulfills the requirements of a metric space, which is a clear improvement to the currently used procedure. The performance of the algorithm is validated first by using data measured from a hot strip mill and then by using synthetically generated trajectories. The new algorithm outperforms the currently used procedure by several orders of magnitude, depending on the context of usage.