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LSTM Networks for Time Series Anomaly Detection by william is a document available to read on EtoBox.

What is LSTM Networks for Time Series Anomaly Detection about?

The document summarizes a research paper that uses stacked long short-term memory (LSTM) neural networks for anomaly detection in time series data. Specifically, it: 1) Trains stacked LSTM networks to predict future time steps in non-anomalous training data and models the prediction errors as a multivariate Gaussian distribution. 2) Uses the error distribution to assess the likelihood that a new data point exhibits anomalous behavior. 3) Demonstrates the approach on four real-world datasets and shows i

Author

william

Language

EN