Large data series: Modeling the usual to identify the unusual

Computational Statistics and Data Analysis

Volumn 32, Issue 3-4, 2000, Pages 245-258

  Downing, D J ^a   Fedorov, V V ^a   Lawkins, W F ^b   Morris, M D ^c   Ostrouchov, G ^b  

^a GLAXOSMITHKLINE   (United Kingdom)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c Iowa State University   (United States)

Author keywords

Data reduction; Feature extraction; Local models; Nonlinear dynamical model; Principal components; Segmentation

Indexed keywords

DATA STORAGE EQUIPMENT; FEATURE EXTRACTION; MATHEMATICAL MODELS; PARALLEL PROCESSING SYSTEMS; REGRESSION ANALYSIS;

NONLINEAR DYNAMICAL MODELS; PRINCIPAL COMPONENTS;

DATA REDUCTION;

EID: 0033882611     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(99)00079-1     Document Type: Article

References (12)

1. Abarbanel H., Brown R., Sidorowich J., Tsimring L. The analysis of observed chaotic data in physical systems. Rev. Mod. Phys. 65:1993;1331-1392.
2. Banfield J.D., Raftery A.E. Model-based Gaussian and non-Gaussian clustering. Biometrics. 49:1993;803-821.
3. Beran, J., 1994. Statistics for Long-Memory Processes. Chapman & Hall, New York.
4. D.O.E., 1990. Atmospheric radiation measurement program plan. Technical Report DOE / ER-0441, US Department of Energy, Office of Health and Environmental Research, Atmospheric and Climate Research Division, National Technical Information Service, 5285 Port Royal Road, Springfield, VI 22161, USA.
5. Downing, D.J., Fedorov, V., Lawkins, W.F., Morris, M.D., Ostrouchov, G., 1996a. Large datasets: segmentation, feature extraction, and compression. Technical Report ORNL / TM-13114, Oak Ridge National Laboratory.
6. Downing, D.J., Fedorov, V.V., Lawkins, W.F., Morris, M.D., Ostrouchov, G., 1996b. Analysing perturbations and nonstationarity in time series using techniques motivated by the theory of chaotic nonlinear dynamical systems. Technical Report ORNL / TM-13115, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
7. Hively, L.M., Clapp, N.E., Daw, C.S., Lawkins, W.F., Eisenstadt, M.L., 1995. Nonlinear analysis of eeg for epileptic seizures. Technical Report ORNL / TM-12961, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
8. Kolmogorov A.N., Uspenskii V.A. Algorithms and randomness. Theory Probab. Appl. 32:1988;389-412.
9. Lawkins, W.F., Clapp Jr., N.E., Daw, C.S., Hively, L.M., Protopopescu, V., Eisenstadt, M.L., 1996. Modeling and analyzing non-seizure eeg data for patients with epilepsy. Technical Report ORNL / TM-13215, Oak Ridge National Laboratory, Oak Ridge, TN 37831.
10. Scott, D.W., 1992. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York.
11. Shen A. Algorithmic complexity and randomness: recent developments. Theory Probab. Appl. 37:1995;92-97.
12. Statistical Sciences, 1996. S-PLUS Guide to Statistical and Mathematical Analysis, Version 3.4. StatSci, a division of MathSoft Inc., Seattle.