joihn / drift_detection

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Drift detection treated as a neural network regression problem

Winter 2020, semster project at ICT4SM Lab, EPFL

Neural network methods for anomaly detection in time series. In this work, 3 neural nets methods were explored:

  • Boostrap Method
  • Lower-Upper bound estimation (LUBE)
  • LSTM Reconstruction error method

Please refer to the report for more information.

The implementation for the best resulting method (Bootstrap) was made public in this repo.

Requirements:

  • Python 3.x
  • Pytorch
  • Pandas
  • Numpy
  • Matplotlib

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