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Artificial Neural Network relation to socioeconomic events and Variety.

Citation Code

G. E. Vranopoulos, A. Triantafyllidis, M. Yiannopoulou (2016) Putting ATM Cash Requirements into Context: Artificial Neural Network relation to socioeconomic events and Variety, Proceedings of 2015 International Congress on Banking, Economics, Finance, and Business, pp. 526–545.

ISSN 2412-4044

Event

BEFB 2016, International Congress on Banking, Economics, Finance, and Business
Dates: June 24-26, 2016
Location: Sapporo, Japan
Status: Paper Peer Reviewed & Presented

Abstract

Developing a cash demand forecasting model for ATMs’ network is a challenging task since there are substantial fluctuations over time and depends on the location of the ATM.  Variety is one of the principal V’s of Big Data, having context being one of the most important aspects.  In this paper the ATM cash withdrawals datasets have been “placed into context” by incorporating socioeconomic datasets.  This contextual enhancement improves the effectiveness of the ANN model devised.

Attachments:
Download this file (2016 07-07 Certificate.PNG)Certificate of Presentation and Participation[PNG File]1186 kB