BIBLIOGRAFIA

Adams A. S., Thiehen K. A. (1991), Automatic teller machines and the older population, „Applied Ergonomics”, Vol. 22

Amromin E., Chakravorti S. (2007), Debit card and cash usage: a cross-country analysis, Technical report, Federal Reserve Bank of Chicago

Aydin I., Karakose M., Akin E. (2009), The Prediction Algorithm Based on Fuzzy Logic Using Time Series Data Mining Method, „World Academy of Science, Engineering and Technology”, Vol. 51

Boeschoten W. C. (1998), Cash management, payment patterns and the demand for money, „De Economist”, Vol. 146

Brentnall A. R., Crowder M. J., Hand D. J. (2008), A statistical model for the temporal pattern of individual automated teller machine withdrawals, „Appl. Statist”, Vol. 57 (1)

Brentnall A. R., Crowder M. J., Hand D. J. (2010), Predicting the amount individuals withdraw at cash machines using a random effects multinomial model, „Statistical Modelling”, Vol. 10 (2)

Cleveland W. S., Devlin S. J. (1980), Calendar Effects in Monthly Time Series: Detection by Spectrum Analysis and Graphical Methods, „Journal of the American Statistical Association”, Vol. 371 (75)

Findley D. F., Monsell B. C. (2009), Modeling Stock Trading Day Effects Under Flow Day-of-Week Effect Constraints, „Journal of Official Statistics”, Vol. 25 (3)

Findley D. F., Monsell B. C., Bell W. R., Otto M. C., Chen B. C. (1998), New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program, „Journal of Business and Economic Statistics”, Vol. 16 (2)

Findley D. F., Soukup R. J. (1999), On the Spectrum Diagnostics Used by X-12-ARIMA to Indicate the Presence of Trading Day Effects after Modeling or Adjustment, Proceedings of the American Statistical Association, Business and Statistics Section

Findley D. F., Soukup R. J. (2000), Modeling and Model Selection for Moving Holidays, Proceedings of the American Statistical Association, Business and Economics Statistics Section

Findley D. F., Soukup R. J. (2001), Detection and Modeling of Trading Day Effects, in ICES II: Proceedings of the Second International Conference on Economic Surveys

Gill J. M. (1996), Making cash dispensers easier to use, http://www.tiresias.org/research/reports//mcdeu.htm (accessed 8 April 2005)

Grubbs F. E. (1950), Sample criteria for testing outlying observations, „The Annals of Mathematical Statistics”, No. 21 (1) (10.1214/aoms/1177729885)

Gurgul H., Suder M. (2012), Efekt kalendarza wypłat z bankomatów sieci Euronet, „Zeszyty Naukowe Wyższej Szkoły Ekonomii i Informatyki w Krakowie”, nr 8

Gurgul H., Suder M. (2013a), Modeling of Withdrawals from Selected ATMs of the „Euronet” Network, „Managerial Economics”, Vol. 13

Gurgul H., Suder M. (2013b), The properties of ATMs development stages — an empirical analysis, „Statistic in Transition”, Vol. 3

Gurgul H., Suder M. (2013c), Rozkład prawdopodobieństwa dziennych wypłat z bankomatów, „Wiadomości Statystyczne”, nr 4

Hand D. J., Blunt G. (2001), Prospecting for gems in credit card data, IMA, „Journal of Management Mathematics”, Vol. 12

Hastie T., Tibshirani R., Friedman J. H. (2001), The elements of statistical learning: Data mining, inference, and prediction, New York: Springer

Hatta K., Iiyama Y. (1991), Ergonomic study of automatic teller machine operability, „International Journal of Human-Computer Interaction”, Vol. 3

Johnson G. I., Coventry L. (2001), You talking to me? Exploring voice in self-service user interfaces, „International Journal of Human-Computer Interaction”, Vol. 13 (2)

Kumar P., Walia E. (2006), Cash Forecasting: An Introduction of Artificial Neural Networks in Finance, „International Journal of Computer Sciences and Applications”, Vol. 3

Liu L. M. (1980), Analysis of Time Series with Calendar Effects, „Management Science”, Vol. 26

McElroy T. S., Holland S. (2005), A Nonparametric Test for Assessing Spectral Peaks, Research Report 2005—10, „Statistical Research Division”, U. S. Bureau of the Census, Washington D. C.

Mester L. (2009), Changes in the use of electronic means of payment: 1995—2007, „Business Review”, No. Q3

Rogers W., Gilbert D. K., Cabrera E. F. (1997), An analysis of automatic teller machine usage by older adults: A structured interview approach, „Applied Ergonomics”, Vol. 28

Simutis R., Dilijonas D., Bastina L. (2008), Cash demand forecasting for ATM using Neural Networks and support vector regression algorithms, 20th International Conference, EURO Mini Conference, „Continuous Optimization and Knowledge-Based Technologies”, EurOPT-2008, Selected Papers, Vilnius, May 20—23

Simutis R., Dilijonas D., Bastina L., Friman J., Drobinov P. (2007), Optimization of Cash Management for ATM Network, „Information Technology and Control”, Vol. 36 (1A)

Snellman H., Viren M. (2009), ATM networks and cash usage, „Applied Financial Economics”, Vol. 19 (10)

Tadeusiewicz R. (1993), Sieci neuronowe, Akademicka Oficyna Wydawnicza, Warszawa

Teddy S. D., Ng S. K. (2011), Forecasting ATM Cash Demands Using a Local Learning Model of Cerebellar Associative Memory Network, „International Journal of Forecasting”, Vol. 27

Thatcher A., Shaik F., Zimmerman C. (2005), Attitudes of semiliterate and literate bank account holders to the use of automatic teller machines, „International Journal of Industrial Ergonomics”, Vol. 35 (2)

Van der Heiden G. C. (1990), Thirty-something million: Should they be exceptions?, „Human Factors”, Vol. 32 (4)

Venkatesha K., Ravia V., Prinzieb A., Van den Poelb D. (2014), Cash demand forecasting in ATMs by clustering and neural networks, „European Journal of Operational Research”, Vol. 232

Zeliaś A., Pawełek B., Wanat S. (2013), Prognozowanie ekonomiczne, Wydawnictwo Naukowe PWN, Warszawa