Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Nonlinear time series analysis
Nonlinear time series analysis
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
High-Dimensional Similarity Joins
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
An adaptive approach for online segmentation of multi-dimensional mobile data
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
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There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling: the step identifies an autoregressive moving average (ARMA) model of dynamic processes from a time series data; (2) prediction: this step makes k steps ahead prediction based on the ARMA model of the process at a crisp time point. (3) Change-points detection: the step is what fits a piecewise segmented polynomial regressive model to the time series data to determine whether it contains a new change-point. Finally, high performance of the proposed algorithm is demonstrated by comparing with Guralnik-Srivastava algorithm.