Automatic recognition of primitive changes in manufacturing process signals
Pattern Recognition
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Managing Sensor Data on Urban Traffic
ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities
Time series classification based on qualitative space fragmentation
Advanced Engineering Informatics
A symbolic representation method to preserve the characteristic slope of time series
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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Time series data-mining algorithms usually scale poorly with regard to dimensionality. Symbolic representations have proven to be a very effective way to reduce the dimensionality of time series even using simple aggregations over episodes of the same length and a fixed set of symbols. However, computing adaptive symbolic representations would enable more accurate representations of the dataset without compromising the dimensionality reduction. Therefore we propose a new generic framework to compute adaptive Segmentation Based Symbolic Representations (SBSR) of time series. SBSR can be applied to any model but we focus on piecewise constant models (SBSRL0) which are the most commonly used. SBSR are built by computing both the episode boundaries and the symbolic alphabet in order to minimize information loss of the resulting symbolic representation. We also propose a new distance measure for SBSRL0 tightly lower bounding the euclidean distance measure.