C4.5: programs for machine learning
C4.5: programs for machine learning
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Algorithm for Matching Sets of Time Series
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
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
An Indexing Scheme for Fast Similarity Search in Large Time Series Databases
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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A system to support the mining task of sets of time series is presented. A model of a set of time series is constructed by a series of classifiers each defining certain consecutive time points based on the characteristics of particular time points in the series. Matching a previously unknown series with respect to a model is discussed. The architecture of the MSTS-System (Mining of Sets of Time Series) is described. As a distinctive feature the system is implemented as a database application: time series and the models, i.e. series of classifiers, are database objects. As a consequence of this integration, advanced functionality as the manipulation of models and various forms of meta learning can be easily build on top of MSTS.