Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 15, 1997, Vol. 9, No. 8, Pages 1691-1709
(doi: 10.1162/neco.1997.9.8.1691)
© 1997 Massachusetts Institute of Technology
Time-Series Segmentation Using Predictive Modular Neural Networks
Article PDF (174.85 KB)
Abstract

A predictive modular neural network method is applied to the problem of unsupervised time-series segmentation. The method consists of the concurrent application of two algorithms: one for source identification, the other for time-series classification. The source identification algorithm discovers the sources generating the time series, assigns data to each source, and trains one predictor for each source. The classification algorithm recursively computes a credit function for each source, based on the competition of the respective predictors, according to their predictive accuracy; the credit function is used for classification of the time-series observation at each time step. The method is tested by numerical experiments.