Real-Time Segmenting Time Series Data
- 516 Downloads
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.
Unable to display preview. Download preview PDF.
- 1.Agrawal R., Faloutsos C., Swami A.: Efficient Similarity Search In Sequence Databases. In: Proc of the 4th Conf on FODO, 1993, 69–84Google Scholar
- 5.Keogh E., Chu S., Hart D., et al.: An Online Algorithm for Segmenting Time Series. In: IEEE Int’l Conf on Data Mining (2001)Google Scholar
- 6.Guralnik V., Srivastava J.: Event Detection from Time Series Data. In: Proc of SIGKDD (1999) 33–42Google Scholar
- 7.Agrawal R., Lin K. I., Sawhney H. S., Shim K.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In Proc of the 21st VLDB (1995) 490–50Google Scholar
- 8.Faloutsos C., Ranganathan M., Manolopoulos Y.: Fast Subsequence Matching in Time-Series Databases. In Proc. of the ACM SIGMOD Conf. on Management of Data (1994) 419–429Google Scholar
- 9.Chan K. P., Fu A. W.: Efficient Time Series Matching by Wavelets. In Proc of the 15th ICDE (1999)Google Scholar
- 10.Perng C. S., Wang H. X., Zhang S. R., et al: Landmarks: A New Model for Similarity-Based Pattern in Time Series Databases. In Proc of the 16th IEEE Int’l Conf on Data Engineering (2000) 475–693Google Scholar