Learning to Recognize Time Series: Combining ARMA models with Memory-based Learning

Kan Deng, Andrew Moore, and Michael Nechyba
IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, 1997, pp. 246 - 250.


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Abstract
For a given time series observation sequence, we can estimate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will be separable into different groups, due to the different underlying mechanisms that generate differing time series. We can then use classification algorithms to predict the class of a new, uncategorized time series. For the purposes of a highly autonomous system, our approach to this classification uses memory-based learning and intensive cross-validation for feature and kernel selection. In an example application, we distinguish between driving data of a skilled, sober driver vs. a drunk driver, by calculating the ARMA model for the respective time series. In this paper, we first give a brief introduction to the theory of time series. We then discuss in detail our approach to time series recognition, using the ARMA model, and finish with experimental results.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference
Kan Deng, Andrew Moore, and Michael Nechyba, "Learning to Recognize Time Series: Combining ARMA models with Memory-based Learning," IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, 1997, pp. 246 - 250.

BibTeX Reference
@inproceedings{Deng_1997_934,
   author = "Kan Deng and Andrew Moore and Michael Nechyba",
   title = "Learning to Recognize Time Series: Combining ARMA models with Memory-based Learning",
   booktitle = "IEEE Int. Symp. on Computational Intelligence in Robotics and Automation",
   pages = "246 - 250",
   year = "1997",
   volume = "1",
}