Valery Guralnik
- Att54.F. Attneave. Some informational aspects of visual perception. Psychol. Rev., 61:183- 193, 1954.Google Scholar
Cross Ref
- CGS92.B.P. Carlin, A.E. Gelfand, and A.F. Smith. Hierarchical bayesian analysis of changepoint problems. Journal of Applied Statistics, 41(2):389-405, 1992.Google ScholarCross Ref
- CM98.Vladimir Cherkassky and Filip Mulier. Learning from Data. Wiley-interscience, New York, N.Y., 1998.Google Scholar
- Gut74.S.B. Guthery. Partition regression. Amer. Statist. Ass., 69:945-947, 1974.Google ScholarCross Ref
- GWS98.Valery Guralnik, Duminda Wijesekera, and Jaideep Srivastava. Pattern directed mining of sequence data. In The Fourth International Coference on Knowledge Discovery and Data Mining, 1998.Google ScholarDigital Library
- Haw76.Douglas M. Hawkins. Point estimation of parameters of piecewise regression models. The Journal of the Royal Statistical Society Series C (Applied Statistics), 25(1):51-57, 1976.Google Scholar
- HKM+96.K. Hatonen, M. Klemettineen, H. Mannila, P. Ronkainen, and H. Toivon en. Knowledge discovery from telecommunication network alarm databases. In Proc. of the 12th Int'l Conf. on Data Eng., pages 115-122, Kyoto, Japan, 1996. Google ScholarDigital Library
- HM73.D.M. Hawkins and D.F. Merriam. Optimal zonation of digitized sequential data. Mathematical Geology, 5(4):389-395, 1973.Google ScholarCross Ref
- Hud66.D.J. Hudson. Fitting segmented curves whose joint points have to be estimated. J. Amer. Statist. Ass., 61:1097-1125, 1966.Google ScholarCross Ref
- Hus93.Marie Huskova. Nonparametric procedures for detecting a change in simple linear regression models. In Applied Change Point Problems in Statistics, 1993.Google Scholar
- KC96.David Kincaid and Ward Cheney. Numerical Analysis. Brooks/Cole Publishing Company, Pacific Grove, CA, 1996.Google Scholar
- KS97.Eamonn Keogh and Padhraic Smyth. A probabilistic approach to fast pattern matching in time series databases. In Third International Conference on Knowledge Discovery and Data Mining, 1997.Google ScholarDigital Library
- Kue94.Robert O. Kuehl. Statistical Principles of Research Design and Analysis. Wadsworth Publishing Company, Belmont, California, 1994.Google Scholar
- MT96.H. Mannila and H. Toivonen. Discovering generalized episodes using minimal occurrences. In Proc. of ~nd Int'l Conference on Knowledge Discovery and Data Mining, pages 146-151, Portland, Oregon, 1996.Google Scholar
- MTV95.H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episodes in sequences. In Proc. of the First Int'l Conference on Knowledge Discovery and Data Mining, pages 210-215, Montreal, Quebec, 1995.Google Scholar
- MTV97.H. Mannila, H. Toivonen, and A.I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discover, 1(3):259-289, November 1997. Google ScholarDigital Library
- PT96.B. Padmanabham and A. Tuzhilin. Pattern discovery in temporal databases: A temporal logic approach, in Pvoc. of 2nd Int'l Conference on Knowledge Discovery and Data Mining, pages 351-354, 1996.Google Scholar
- Raf93.Adrian E. Raftery. Change point and change curve modeling in stochastic processes and spatial statistics. Technical Report 23, University of Washington, 1993.Google Scholar
- SA95.R. Srikant and R. Agrawal. Mining generalized association rules. In Proc. of the 21th VLDB Conference, pages 407-419, Zurich, Switzerland, 1995. Google ScholarDigital Library
- SO94.N. Sugiura and Todd Ogden. Testing change-points with linear trend. Communications in Statistics B:Simulation and Computation, 23:287-322, 1994.Google ScholarCross Ref
Index Terms
- Event detection from time series data
Recommendations
An anomaly detection framework for time series data: An interval-based approach
AbstractDue to the high data volume and non-stationarity of time series data, it is very difficult to directly use the original data for anomaly detection. In this study, a novel framework of anomaly detection is proposed, whose intent is to ...
Anomaly detection via a combination model in time series data
AbstractSince the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in ...
Comments