Recurrent Dynamical Projection for Time Series-Based Fraud Detection

  • Eric A. Antonelo
  • Radu State
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614)


A Reservoir Computing approach is used in this work for generating a rich nonlinear spatial feature from the dynamical projection of a limited-size input time series. The final state of the Recurrent neural network (RNN) forms the feature subsequently used as input to a regressor or classifier (such as Random Forest or Least Squares). This proposed method is used for fraud detection in the energy distribution domain, namely, detection of non-technical loss (NTL) using a real-world dataset containing only the monthly energy consumption time series of (more than 300 K) users. The heterogeneity of user profiles is dealt with a clustering approach, where the cluster id is also input to the classifier. Experimental results shows that the proposed recurrent feature generator is able to extract relevant nonlinear transformations of the raw time series without a priori knowledge and perform as good as (and sometimes better than) baseline models with handcrafted features.


Recurrent neural networks Reservoir computing Non-technical loss Eletricity fraud detection Clustering Energy distribution networks 



The authors would like thank Jorge Meira and Patrick Glauner from University of Luxembourg, and Lautaro Dolberg, Yves Rangoni, Franck Bettinger and Diogo M. Duarte from Choice Technologies for useful discussions on NTL.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Interdisciplinary Centre for Security, Reliability and TrustUniversity of LuxembourgLuxembourg CityLuxembourg

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