Improvement of demand forecasting models with special days
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Forecasting ATM cash demands is a challenging research task. When the forecasting results are too high compared to the real demand, this will cause excessive cash at bank's ATMs and the cost of lost interest. On the other hand, if the forecast is too low, this will result in dissatisfaction of bank customers because of cash-outs. Although recent studies focused on new computational intelligence techniques for cash demand forecasting, this paper advocates the enhancement of the dataset to improve the prediction performance of forecasting models. In this study, 19 special days in the UK have been considered and NN5 competition dataset, which includes 735 daily withdrawal amounts from 111 ATMs in UK, was updated with these calendar days. After preprocessing step and application of exponential smoothing method, we achieved 21.57 % average SMAPE for 56 days forecasting horizon. This study shows that good forecasting results can be reached by improving the data even if we do not apply complex computational intelligence techniques. (C) 2015 The Authors. Published by Elsevier B.V.