Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Fast similarity search in the presence of longitudinal scaling in time series databases
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Subsequence matching on structured time series data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Adaptive Data Delivery Framework for Financial Time Series Visualization
ICMB '05 Proceedings of the International Conference on Mobile Business
Incremental stock time series data delivery and visualization
Proceedings of the 14th ACM international conference on Information and knowledge management
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Time series subsequence matching based on a combination of PIP and clipping
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
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Subsequence searching is a non-trivial task in time series data analysis and mining. In recent years, different approaches are published to improve the performance of subsequence searching which based on index the time series and lower bound the Euclidean distance. In this paper, the problem of applying Euclidean distance on time series similarity measure is first reviewed. Previous approaches to align time series for similarity measure are then adopted for subsequence searching, they include: dynamic time warping (DTW) and perceptually important point (PIP). Furthermore, a tree data structure (SB-Tree) is developed to store the PIP of a time series and an approximate approach is proposed for subsequence searching in the SB-Tree. The experimental results performed on both synthetic and real datasets showed that the PIP approach outperformed DTW. The approximate approach based on SB-Tree can further improve the performance of the PIP-based subsequence searching while the accuracy can still be maintained.