Long Short Term Memory Networks for Anomaly Detection in Time Series

  title={Long Short Term Memory Networks for Anomaly Detection in Time Series},
  author={Pankaj Malhotra and Lovekesh Vig and Gautam Shroff and Puneet Agarwal},
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. Stacking recurrent hidden layers in such networks also enables the learning of higher level temporal features, for faster learning with sparser representations. In this paper, we use stacked LSTM networks for anomaly/fault detection in time series. A network is trained on non-anomalous… CONTINUE READING
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  • D. George
  • PhD Thesis, Stanford University
  • 2008
1 Excerpt

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  • P. Hayton
  • Philosophical Transactions of the Royal Society…
  • 2007
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