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
Time-series similarity problems and well-separated geometric sets
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
PKDD '97 Proceedings of the First European Symposium 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
MSTS: A System for Mining Sets of Time Series
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Spatial feature based recognition of human dynamics in video sequences
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
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Time series are time-stamped sequences of values which represent a parameter of the observed processes in subsequent time points. Given a set of time series describing a set of similar processes, the model of the behavior of processes is constructed as a range of classification trees which describe the characteristics of each particular time point in series. An algorithm for matching a sequence of values with the model is used for searching common patterns in the sets of time series, and for predicting the starting time points of undated time series. The algorithm was developed and analyzed in the frame of the study of tree-ring time series. The implementation and the empirical analysis of the algorithm on the tree-ring time series are presented.