Corpus ID: 43680425

Long Short Term Memory Networks for Anomaly Detection in Time Series

@inproceedings{Malhotra2015LongST,
  title={Long Short Term Memory Networks for Anomaly Detection in Time Series},
  author={P. Malhotra and L. Vig and G. Shroff and Puneet Agarwal},
  booktitle={ESANN},
  year={2015}
}
  • P. Malhotra, L. Vig, +1 author Puneet Agarwal
  • Published in ESANN 2015
  • Computer Science
  • Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long term memory. [...] Key Method A network is trained on non-anomalous data and used as a predictor over a number of time steps. The resulting prediction errors are modeled as a multivariate Gaussian distribution, which is used to assess the likelihood of anomalous behavior. The efficacy of this approach is…Expand Abstract
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