PL EN


2015 | 4(42) | 165-177
Article title

Modelling and Forecasting Cash Withdrawals in the Bank

Title variants
PL
Modelowanie i prognozowanie wypłat gotówki w banku
Languages of publication
EN
Abstracts
EN
The goal of the paper is searching for the optimal forecasting model estimating the amount of cash withdrawn daily by the customers of one of the Polish banks by means of statistical and machine learning methods. The methodology of model creation and assessment criteria are significantly different for the models considered in the paper — i.e., for ARMAX and MLP. However, the comparison of forecasts generated by both models seems to be useful. Variables and attributes reflecting the calendar effects, used in both models, in case of obtained errors of forecasts less than 20%, showed a significant, non-linear influence of this type of predictors on the amount of the daily cash withdrawals at the bank, and hence on the amount of the daily declared cash limit.
PL
Prezentowane badania są próbą poszukiwania optymalnego modelu prognostycznego do oszacowania dziennej ilości gotówki pobieranej przez klientów jednego z polskich banków za pomocą metod statystycznych oraz metod uczenia maszynowego. Metodologia tworzenia modelu i kryteria oceny znacząco różnią się dla zastosowanych i opisanych w artykule modeli (ARMAX i MLP). Z tego powodu porównanie prognoz generowanych przez oba modele wydaje się być szczególnie interesujące i przydatne. Zmienne i atrybuty odzwierciedlające efekty kalendarzowe, użyte w obu modelach, w przypadku uzyskanych błędów prognoz poniżej 20%, wykazały znaczny, nieliniowy wpływ tego typu czynników predykcyjnych na wysokość dziennych wypłat gotówki, a więc pośrednio na wielkość limitu dziennego stanu gotówki w banku.
Contributors
  • University of Management and Administration in Zamość, Poland
author
  • University of Management and Administration in Zamość, Poland
  • University of Information Technology and Management in Rzeszów, Poland
  • University of Rzeszów, Poland
References
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-b89f3705-a050-4a5d-9d7a-ba75bd4d25d5
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.