Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A fast algorithm for computing longest common subsequences
Communications of the ACM
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Scaling up Dynamic Time Warping to Massive Dataset
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Search Over Time-Series Data Using Wavelets
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IEEE Transactions on Information Theory
Indexable PLA for efficient similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proceedings of the VLDB Endowment
Faster retrieval with a two-pass dynamic-time-warping lower bound
Pattern Recognition
Fast Object Tracking in Intelligent Surveillance System
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Online constrained pattern detection over streams
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Searching trajectories by locations: an efficiency study
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Benchmarking dynamic time warping for music retrieval
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
A framework for time-series analysis
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
A review on time series data mining
Engineering Applications of Artificial Intelligence
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
Weighted dynamic time warping for time series classification
Pattern Recognition
Embedding-based subsequence matching in time-series databases
ACM Transactions on Database Systems (TODS)
The Journal of Machine Learning Research
Interactive hybrid simulation of large-scale traffic
Proceedings of the 2011 SIGGRAPH Asia Conference
User oriented trajectory search for trip recommendation
Proceedings of the 15th International Conference on Extending Database Technology
Time-series mining in a psychological domain
Proceedings of the Fifth Balkan Conference in Informatics
ACM Computing Surveys (CSUR)
Experimental comparison of representation methods and distance measures for time series data
Data Mining and Knowledge Discovery
A representation of time series based on implicit polynomial curve
Pattern Recognition Letters
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
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A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified ε threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, time shifts, and data scaling. Our work proposes a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate such threshold value techniques, including LCSS and EDR. Using FTSE, we show that these techniques can be evaluated faster than using either traditional dynamic programming or even warp-restricting methods such as the Sakoe-Chiba band and the Itakura Parallelogram. We also show that FTSE can be used in a framework that can evaluate a richer range of ε threshold-based scoring techniques, of which EDR and LCSS are just two examples. This framework, called Swale, extends the ε threshold-based scoring techniques to include arbitrary match rewards and gap penalties. Through extensive empirical evaluation, we show that Swale can obtain greater accuracy than existing methods.