Bielak, J. 2010. “Prognozowanie rynku pracy woj. lubelskiego z wykorzystaniem modeli ARIMA i ARIMAX.” Barometr Regionalny. Analizy i prognozy no. 1 (19):27–44.
Brockwell, P.J., and R.A. Davis. 2002. Introduction to Time Series and Forecasting. 2nd ed., Springer texts in statistics. New York: Springer.
DOI: https://doi.org/10.1007/b97391
Cieślak, M. 2001. Prognozowanie gospodarcze. Metody i zastosowania. Warszawa: Wydawnictwo Naukowe PWN.
Cleveland, W.S., and S.J. Devlin. 1980. “Calendar Effects in Monthly Time-Series — Detection by Spectrum Analysis and Graphical Methods.” Journal of the American Statistical Association no. 75 (371):487–496. doi: 10.2307/2287636.
DOI: https://doi.org/10.1080/01621459.1980.10477500
Cottrell, A., and R. Lucchetti “Jack”. 2015. “Gretl User’s Guide. Gnu Regression, Econometrics and Time-Series Library.”
Darwish, S.M. 2013. “A Methodology to Improve Cash Demand Forecasting for ATM Network.” International Journal of Computer and Electrical Engineering no. 5 (4):405–409. doi: 10.7763/IJCEE.2013.V5.741.
DOI: https://doi.org/10.7763/IJCEE.2013.V5.741
Esteves, P.S., and P.M.M. Rodrigues. 2010. “Calendar Effects in Daily ATM Withdrawals.” Banco de Portugal. Working Papers (12):1–16, i-iv.
Fausett, L.V. 1994. Fundamentals of Neural Networks. Architectures, Algorithms, and Applications. Englewood Cliffs, NJ: Prentice-Hall.
Gurgul, H., and M. Suder. 2013a. “The Properties of ATMs Development Stages — an Empirical Analysis.” Statistics in Transition no. 14 (3):443–466
Gurgul, H., and M. Suder. 2013b. “Rozkład prawdopodobieństwa dziennych wypłat z bankomatów.” Wiadomości Statystyczne no. 58 (4):1–22.
Gurgul, H., and M. Suder. 2015. “Prognozowanie wypłat z bankomatów.” Wiadomości Statystyczne no. 60 (8):25–48.
DOI: https://doi.org/10.5604/01.3001.0014.8305
Kufel, T. 2011. Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. 3rd ed. Warszawa: Wydawnictwo Naukowe PWN.
Lee, M.H., Suhartono, and N.A. Hamzah. 2010. “Calendar Variation Model Based on ARIMAX for Forecasting Sales Data with Ramadhan Effect.” In Proceedings of the Regional Conference on Statistical Sciences 2010, edited by I. Ab Ghani, A.G. Hussin, I. Mohamed, Y.B. Wah and S.M. Deni, 349–361. Malaysia Institute of Statistics, Universiti Teknologi MARA.
Liu, L.M. 1980. “Analysis of Time-Series with Calendar Effects.” Management Science no. 26 (1):106–112. doi: 10.1287/mnsc.26.1.106.
DOI: https://doi.org/10.1287/mnsc.26.1.106
Rumelhart, D.E., and J.L. McClelland. 1986. Parallel Distributed Processing. Explorations in the Microstructure of Cognition. 2 vols, Computational Models of Cognition and Perception. Cambridge, Mass.: MIT Press.
DOI: https://doi.org/10.7551/mitpress/5236.001.0001
Simutis, R., D. Dilijonas, and L. Bastina. 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’2008:416–421.
StatSoft Inc. 2013. “Electronic Statistics Textbook.” Tulsa, OK: StatSoft. http://www.statsoft.com/textbook/.
Theil, H. 1961. Economic Forecasting and Policy. Amsterdam: North-Holland Pub. Co.
Theil, H. 1966. Applied Economic Forecasting, Studies in Mathematical and Managerial Economics. Amsterdam-Chicago: North-Holland Pub. Co.; Rand McNally.
Venkatesh, K., V. Ravi, A. Prinzie, and D. Van den Poel. 2014. “Cash Demand Forecasting in ATMs by Clustering and Neural Networks.” European Journal of Operational Research no. 232 (2):383–392. doi: 10.1016/j.ejor.2013.07.027.
DOI: https://doi.org/10.1016/j.ejor.2013.07.027