Site maintenance: Ilka Miloucheva

• R.Agrawal,G.Psaila,E.L.Wimmers,M.Zaot,Querying Shapes of Histories
  Twenty-first International Conference on Very Large DatabasesVLDB '95,Morgan Kaufmann Publishers, Inc. San Francisco,USA,1995
• R.Agrawal,R.Lin,Sawhney,Shim,Fast similarity search in the presence of noice,scaling and translation in time-series databases. Twenty-first International Conference on Very Large DatabasesVLDB '95,Morgan Kaufmann Publishers, Inc. San Francisco,USA,1995
• R.Agrawal,R.Srikant,Mining sequential patterns,Eleventh international conference on data engineering,Taipei,Taiwan,IEEE,Computer Society Press 3-14,1995
• T.Abraham,J.Roddick,Research Issues in spatio-temporal knowledge discovery .In Proc Sigmod97,Arizona,Usa,1997
• Abraham, T. and Roddick, J.F. ‘Opportunities for knowledge discovery in spatio-temporal information systems’. Aust. J. Inf. Syst. 5(2):3-12, 1998.
• P.Brockwell,A.Davis,Introduction to time series and forecasting ,New York, Springer, (Springer texts in statistics),1996.
• P.Brockwell,A.Davis, Time series:theory and methods, 2.edition,New York, Springer, (Springer series in statistics),1991
• D.Berndt,J.Clifford,Finding Patterns in time-series,a dynamic programming approach,In Advances in Knowledge Discovery and Data Mining,MIT Press pp 229-248,1996
• C.Chatfield,The analysis of time series:theory and practice,1ed, London,Chapman and Hall, (Monographs on applied probability and statistics),1975
• Les Cortell, Connie Logg, Throughput Time Series Patterns (Diurnal and Step Functions), http://www.slac.stanford.edu/comp/net/pattern/diurnal.html.
• Chartpatterns,http://www.chartpatterns.com
• D. Goldin and P. Kanellakis. On similarity queries for time-series data: constraint specification and implementations. In Int. Conf. on the Principles and Practice of Constraint Programming,1995.
• G.Das,D.Gunopulos, H.Mannila.Finding Similar Time Series, In Principles of Data Mining and Knowledge Discovery, pages 88--100,1997.
• Cristian Estan, Stefan Savage and George Varghese,
  Automatically Inferring Patterns of Resource Consumption in Network Traffic, to appear in Proceedings of the ACM SIGCOMM Conference, Karlsruhe, Germany, August 2003.
• C.Faloutsos,M.Ranganathan,Y.Manopoulos,Fast subsequence matching in time series databases.In Proc ACM Sigmod Conference on the management of Data, Minneapolis,1994
• N.K. Groschwitz and G.C. Polyzos, "A Time-Series Model of Long-term Traffic on the NSFNET Backbone," Proc. IEEE International Conference on Communications (ICC'94), New Orleans, LA, pp. 1400-1404, May 1994.
• J.Han,M.Kamber,Data Mining,Concepts and Techniques,San Francisco,Calif.,Morgan Kaufmann,2001
  (SubChapter 9.4 Mining Time-Series and Sequence Data,SubChapter 8.9 & 1.4.5. Outlier Analysis , SubChapter 1.5 Are all of the Patterns interesting?,Chapter 10.3.3 Theoretical Foundations of Data Mining)
• David Hand,Heikki Mannila,Padhraic Smith,Principles of Data Mining,Massachutes,MIT Press,2001
  (Chapter 6:Models and Patterns pp:165-208,Chapter 8:Search Methods,Chapter 13:Finding Patterns and Rules,Chapter 14 Retrieval by content ,SubChapter 14.6 Time Series and Sequence Retrieval )
• Han,J.Gong,Y.Yin,Mining segment-wise periodic patterns in time related databases.In Proc forth international conference on knowledge discovery and data mining ,AAAI Press,Menlo Park 214-218,1995
• Han,Dong,Y.Yin. Efficient mining of partial periodic patterns in time series database. Int. Conf. Data Engineering (ICDE'99), Sydney, Australia, April 1999
• M.V.Joshi,G.Karypis,V.Kumar,A Universal Formulation of Sequential Patterns
  Technical Report Under Preparation, Department of Computer Science, University of Minnesota, Minneapolis, 1999.
• E.Keogh,Smyt,A probabilistic approach to fast pattern matching in time series databases.In Proc third international conference on knowledge discovery and data mining ,California,AAAI Press,Menlo Park,California 24-30,1997
• M.Kendall,J.Ord,Time Series,Charles Griffin Book,London and High Wycombe, August 1990
• Eamonn Keogh and Padhraic Smyth, `A Probabilistic Approach to Fast pattern matching in Time Series Databases,' KDD '97.
• Eamonn Keogh, Stefano Lonardi, Bill Chiu, Finding Surprising Patterns in a Time Series Database in Linear Time and Space, SIGKDD 02, Edmonton, Canada, July, 2002.
• H.Mannila,H.Toivonen,Verkamo,Discovering frequent episodes in sequences.In proc of the first international conference on KDD-95,Montreal,Quebec,Canada,AAAI Press,Menloe Park,California 210-215,1995
• NIST/SEMATECHe-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/
• T. Oates, M. D. Schmill, D. Jensen,P. R. Cohen,A family of algorithms for finding temporal structure in data,In 6th Intl. Workshop on AI and Statistics, March 1997
• T.Oates,R. Cohen.,Searching for structure in multiple streams of data,Thirteenth International Conference on Machine Learning, 1996.
• T. Oates, M.D. Schmill. D.E. Gregory, P.R. Cohen. Detecting complex dependencies in categorical data. In Finding Structure in Data : Artificial intelligence and Statistics, p.185-195, 1995
• R.Povinelli,Time Series Data Mining,Identifying Temporal Patterns for characterization and prediction of time series events,Dissertation submitted by Richard Povinelli,Marquette University,Milwankee,Winsconsin,December 1999
• C. S. Perng, H. Wang, S. R. Zhang, D. S. Parker,Landmarks: a new model for similarity based pattern querying in time series databases, In Proc. 16th Int. Conf. on Data Engineering , San Diego, USA, 2000.
• O.Rud,Data Mining Cookbook,Published by John Wiley&Sons,Inc,605 Third Avenue,New York,2001
• D.Rafei,A.Mendelson,Querying Time Series Data Based on Similarity,IEEE Transactions on Knowledge and Data Engineering,October 2000
• John F. Roddick, Myra Spiliopoulou, A Bibliography of Temporal, Spatial and Spatio-Temporal Data Mining Research SIGKDD Explorations, ACM SIGKDD, June 1999.
• R.H.Shumway,S. Stoffer,Time series analysis and its applications,New York,Springer, (Springer texts in statistics), 2000
• TheStatisticsHomepage,Statsoft,Electronictextbook, http://www.statsoftinc.com/textbook/stathome.html
• Programs :Tramo Time Series Regression with Arima Noise,Missing Observations and Outliers and Seats Signal Extraction in Arima Time Series,Instructions for the user,Victor Gomez,A.Maravall,November 1997
• Weigend, Andreas S, Time series prediction : forecasting the future and understanding the past ,proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis,Santa Fe, New Mexico, May ,1992
• K.Wang,Discovering patterns from large and dynamic sequential data,Intel.Inf.Syst 88-33 ,1997
• Wan Gons,Periodic Search on Time Related Data Set,In Master Thesis,Simon Fraser University,November 1997
• Xianping Ge Xge,Pattern Matching in Financial Time Series Data,url=citeseer.nj.nec.com/478619.html
• B. Xia. Similarity Search in Time Series Data Sets. In Master thesis, Simon Fraser University, 1997.
• B.-K. Yi, N.D. Sidiropoulos, T. Johnson, H.V. Jagadish. C. Faloutsos. A. Biliris, Online Data Mining for Co-volving Time Series, ICDE 2000.
• P. Barford and D. Plonka, “Characteristics of network traffic flow anomalies,” in Proceedings of ACM SIGCOMM Internet Measurement Workshop,San Francisco, CA, November 2001.