Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)

Bachelor thesis English OPEN
Singh, Akash;
(2017)
  • Publisher: KTH, Skolan för informations- och kommunikationsteknik (ICT)
  • Subject: LSTM; RNN; anomaly detection; time series; deep learning | LSTM; RNN; avvikelsedetektion; tidsserier; djupt lärande | Computer Sciences | Datavetenskap (datalogi)

We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal t... View more
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