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  1. A PyTorch implementation of an autoencoder trained to minimize the Mahalanobis distance between input and reconstruction.

    Python 14 2

  2. ANN2 Public

    Neural Networks package for R with a fast C++ back-end and special support for unsupervised anomaly detection using autoencoders

    C++ 9 3

  3. An embedding for categorical data that is well suited for anomaly detection.

    Jupyter Notebook 1

  4. A hierarchical (in a Bayesian way) implementation of FB Prophet. Can be used to model multiple correlated timeseries simultaneously

    Jupyter Notebook

  5. Tools to help with making automated trading decisions, executing orders and backtesting new strategies

    Python

  6. Code to benchmark several unsupervised anomaly detection models on various data sets

    Jupyter Notebook

242 contributions in the last year

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Contribution activity

December 2021

Created 3 commits in 1 repository
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