Prediction of time varying composite sources by temporal fuzzy clustering

We present a method for predicting non-stationary signals generated by a time varying composite source. The method is based on the concept of temporal fuzzy clustering. A fuzzy clustering algorithm is applied to the given part (past+present) of the time series and the calculated clusters and members...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563) pp. 329 - 332
Main Authors Policker, S., Geva, A.B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Biological system modeling
Clustering algorithms
Economic forecasting
Hidden Markov models
Pattern analysis
Predictive models
Probability distribution
Signal generators
Switches
Time measurement
Online AccessGet full text

Cover

More Information
Summary:We present a method for predicting non-stationary signals generated by a time varying composite source. The method is based on the concept of temporal fuzzy clustering. A fuzzy clustering algorithm is applied to the given part (past+present) of the time series and the calculated clusters and membership matrix are then used to estimate a mixture probability distribution function (PDF) underlying the series. In this way a continuous drift in the series distribution expressed as a drift in the clusters' appearance rate can be estimated. A future PDF can then be predicted by fitting a specific model to the estimated past and future PDF values. This also enables the generation of a minimal-mean-squared-error prediction for a future time series element using the estimated mean value of the predicted PDF.
ISBN:9780780370111
0780370112
DOI:10.1109/SSP.2001.955289