Investigation of CI forecasting algorithms for short-time cash demand in ATM network
نویسندگان
چکیده
Good ATM network cash management requires accurate information of future cash demand. In this paper we compare computational intelligence models when performing cash flow forecasting for one day. Adaptive input selection and model parameter identification are used with every forecasting model in order to perform more flexible comparison. Experimental data contains 200 ATMs from real ATM network with historical period of 26 months. Investigation of historical data length influence for forecasting accuracy with every model is also performed. Results suggest -SVR (support vector regression) forecasting model performs best when SMAPE forecasting accuracy measure is used.
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