A flexible neural network for ATM cash demand forecasting

  • Authors:

  • Rimvydas Simutis;Darius Dilijonas;Lidija Bastina;Josif Friman

  • Affiliations:

  • Kaunas University of Technology, AVATI, Kaunas, Lithuania and Vilnius University, Kaunas Faculty of Humanities, Kaunas, Lithuania;Vilnius University, Kaunas Faculty of Humanities, Kaunas, Lithuania;JSC "Penkiu kontinentu bankinės technologijos", Vilnius, Lithuania;JSC "Penkiu kontinentu bankinės technologijos", Vilnius, Lithuania

  • Venue:
  • CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
  • Year:
  • 2007

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Abstract

The paper presents an artificial neural network based approach in support of cash demand forecasting for automatic teller machine (ATM). On the start phase a three layer feed-forward neural network was trained using Levenberg-Marquardt algorithm and historical data sets. Then ANN was retuned every week using the last observations from ATM. The generalization properties of the ANN were improved using regularization term which penalizes large values of the ANN weights. Regularization term was adapted online depending on complexity of relationship between input and output variables. Performed simulation and experimental tests have showed good forecasting capacities of ANN. At current stage the proposed procedure is in the implementing phase for cash management tasks in ATM network.