Time-Series Segmentation Using Predictive Modular Neural Networks
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.
Cite
Text
Kehagias and Petridis. "Time-Series Segmentation Using Predictive Modular Neural Networks." Neural Computation, 1997. doi:10.1162/NECO.1997.9.8.1691Markdown
[Kehagias and Petridis. "Time-Series Segmentation Using Predictive Modular Neural Networks." Neural Computation, 1997.](https://mlanthology.org/neco/1997/kehagias1997neco-timeseries/) doi:10.1162/NECO.1997.9.8.1691BibTeX
@article{kehagias1997neco-timeseries,
title = {{Time-Series Segmentation Using Predictive Modular Neural Networks}},
author = {Kehagias, Athanasios and Petridis, Vassilios},
journal = {Neural Computation},
year = {1997},
pages = {1691-1709},
doi = {10.1162/NECO.1997.9.8.1691},
volume = {9},
url = {https://mlanthology.org/neco/1997/kehagias1997neco-timeseries/}
}