Forecasting Cash Demand in Atm Using Neural Networks and Least Square Support Vector Machine

In this work we forecast the daily ATM cash demand using dynamic models of type Nonlinear Autoregressive Exogeneous inputs (NARX) and Nonlinear Autoreggressive Moving Average with Exogeneous Inputs (NARMAX) performed by Neural Networks (NN) and Least Square Support Vector Machine (LS-SVM) and used to predict one step (OSA) or multistep (MPO). The aim is to compare which model perform better results. We found that the Multilayer Perceptron NN presented the best index of agreement with an average of 0.87 in NARX-OSA and 0.85 in NARX-MPO. After, Radial Basis Function NN was 0.82 for both cases. Finally, LS-SVM obtained the worst results with 0.78 for NARX-OSA and 0.70 for NARX-MPO. No significant differences between NARX and NARMAX structures were found. Our contribution would have obtained the 2nd place in the NN5 competition of computational methods. © 2011 Springer-Verlag.