Predicting Failures with Hidden Markov Models
نویسنده
چکیده
A key challenge for proactive handling of faults is the prediction of system failures. The main principle of the approach presented here is to identify and recognize patterns of errors that lead to failures. I propose the use of hidden Markov models (HMMs) as they have been successfully used in other pattern recognition tasks. The paper further motivates their use, explains how HMMs can be used to predict failures and describes the training procedure. An outlook to a more sophisticated treatment of time between events is also presented.
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