Recurrent Dynamical Projection for Time Series-Based Fraud Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

References

  1. 1.
    Antonelo, E.A., Schrauwen, B.: On learning navigation behaviors for small mobile robots with reservoir computing architectures. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 763–780 (2015)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Antonelo, E.A., Flesch, C., Schmitz, F.: Reservoir computing for detection of steady state in performance tests of compressors. Neurocomputing (in press)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)MATHGoogle Scholar
  4. 4.
    Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., Green, R.C.: High performance computing for detection of electricity theft. Int. J. Electr. Power Energy Syst. 47, 21–30 (2013)CrossRefGoogle Scholar
  5. 5.
    Glauner, P., Meira, J., Valtchev, P., State, R., Bettinger, F.: The challenge of non-technical loss detection using artificial intelligence: a survey. Int. J. Comput. Intell. Syst. (IJCIS) 10(1), 760–775 (2017)CrossRefGoogle Scholar
  6. 6.
    Heckman, J.J.: Sample selection bias as a specification error. Econometrica 47(1), 153–161 (1979). http://www.jstor.org/stable/1912352 CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report GMD Report 148, German National Research Center for Information Technology (2001)Google Scholar
  8. 8.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  9. 9.
    Meira, J.A., Glauner, P., Valtchev, P., Dolberg, L., Bettinger, F., Duarte, D., et al.: Distilling provider-independent data for general detection of non-technical losses. In: Power and Energy Conference, Illinois, 23–24 February 2017 (2017)Google Scholar
  10. 10.
    Schrauwen, B., Warderman, M., Verstraeten, D., Steil, J.J., Stroobandt, D.: Improving reservoirs using intrinsic plasticity. Neurocomputing 71, 1159–1171 (2008)CrossRefGoogle Scholar
  11. 11.
    Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations