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.1. Adam, Christopher. (2000). The Transactions Demand for Money in
Chile, Research Department of the Central Bank of Chile.
.....
2. Chauvin, Y. and Rumelhart D.E. (1995). Backpropagation: Theoryarchitectures, and applications, Hillsdale, NJ: Erlbaum.
3. Egan, Bob and George Tubin and Charul Vyas. (2007). US MobileBanking Forecast: 2007-2012, www.TowerGroup.com.
4. Engle, Robert F. and Jeffrey R. Russell. (1994). ForecastingTransactions Rates: The Autoregressive Conditional Duration Model,Working Papers, National Bureau of Economic Research, Cambridge.
5. Gan, Christopher and Mike Clemes and Visit Limsombunchai andAmy Weng. (2005), Consumer Choice Prediction: Artificial NeuralNetworks versus Logistic Model, Commerce Division, LincolnUniversity Canterbury, No. 104.
6. Haykin, Simon S. (1994). Neural Networks: A ComprehensiveFoundation, Macmillan College Publishing.
7. Howard, Demuth and Mark Beale. (2007). Neural Network ToolboxUser’s Guide, www.mathworks.com.
8. Maass, Peter and Torsten Koehler and Jan Kalden and Roza Costa andUlrich Parlitz and Christian Merkwirth and Jörg Wichard. (2003).Mathematical methods for forecasting bank transaction data, Zentrumfür Technomathematik.
9. Paul, Justin and Anirban Mukherjee. (2010). ATMs and Cash DemandForecasting: A Study of Two Commercial Banks, Journal of RegionalDevelopment, vol.2, no.2, pp: 653-671.
10. Sharda, R. and R.B Patil. (1992). Connectionist approach to time seriesprediction: An empirical test, Journal of Intelligent Manufacturing 3,pp: 317–323.
11. Simutis, Rimvydas and Darius Dilijonas and Lidija Bastina. (2008).Cash Demand Forecasting for ATM Using Neural Networks andSupport Vector Regression Algorithms, 20th EURO Mini Conference:Continuous Optimization and Knowledge-Based Technologies,Vilnius, Lithuania, pp: 416–421.
12. Snellman, Jussi and Jukka Vesala. (1999). Forecasting theElectronification of Payments with Learning Curves: The Case ofFinland, Discussion Papers, Bank of Finland, Research Department.
13. Tang, Zaiyong and and Fishwick Paul A. (1993), Feedforward NeuralNets as Models for Time Series Forecasting, Journal on Computing,vol.5, no.4, pp:374-385.
14. Zhang, G. and B. Eddy Patuwo and Michael Hu. (1998). Forecastingwith artificial neural network: the state of art, International Journal ofForecasting, vol.14, pp: 35-62.
15. Zhang, Peter .G. (2003). Time series forecasting using a hybridARIMA and neural network model, Neuro Computing, vol.50, pp:159-175