Accession Number:

AD0680824

Title:

UNSUPERVISED SEQUENTIAL CLASSIFICATION OF NONSTATIONARY TIME SERIES.

Descriptive Note:

Final rept. 1 Aug 65-30 Apr 67,

Corporate Author:

PHILCO-FORD CORP BLUE BELL PA COMMUNICATIONS AND ELECTRONICS DIV

Personal Author(s):

• Harley,Thomas J. , Jr

Report Date:

1968-10-01

Pagination or Media Count:

41.0

Abstract:

The problem of unsupervised sequential classification of nonstationary time series is formulated as a compound decision problem. The a priori class probabilities are assumed to be stochastically independent, time varying, and unknown. The class-conditional cumulative distribution functions of the random variable, X, are assumed to be of known parametric form, but with the parameter values unknown and time varying. A Bayesian approach is taken, employing an a priori distribution on the unknown parameters and class probabilities, which leads to a solution in terms of minimizing the sample conditional risk. If the unknown parameters and class probabilities are assumed to have Markov time dependence, then the nonstationary problem can be reformulated in terms of the problem of classifying stationary time series with known parameters and with known Markov dependence on the states-of-nature. Specific results are presented for two special cases - unknown, time varying a priori class probabilities, and unknown time varying mean. Author

Descriptors:

• (*INFORMATION THEORY
• PATTERN RECOGNITION)
• (*PATTERN RECOGNITION
• *TIME SERIES ANALYSIS)
• BIONICS
• DECISION THEORY
• PROBABILITY

Subject Categories:

• Cybernetics
• Bionics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE