An introduction to genetic algorithms
An introduction to genetic algorithms
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
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 Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
SAXually Explicit Images: Finding Unusual Shapes
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
iSAX: disk-aware mining and indexing of massive time series datasets
Data Mining and Knowledge Discovery
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Enhancing the symbolic aggregate approximation method using updated lookup tables
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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Time series data appear in a broad variety of economic, medical, and scientific applications. Because of their high dimensionality, time series data are managed by using representation methods. Symbolic representation has attracted particular attention because of the possibility it offers to benefit from algorithms and techniques of other fields in computer science. The symbolic aggregate approximation method (SAX) is one of the most important symbolic representation techniques of times series data. SAX is based on the assumption of "high Gaussianity" of normalized time series which permits it to use breakpoints obtained from Gaussian lookup tables. The use of these breakpoints is the heart of SAX. In this paper we show that this assumption of Gaussianity oversimplifies the problem and can result in very large errors in time series mining tasks. We present an alternative scheme, based on the genetic algorithms (GASAX), to find the breakpoints. The new scheme does not assume any particular distribution of the data, and it does not require normalizing the data either. We conduct experiments on different datasets and we show that the new scheme clearly outperforms the original scheme.