Forecasting Cash Money Withdrawals Using Wavelet Analysis and Wavelet Neural Networks

The increasing demand for easily accessible cash drives banks to expand their Automatic

Teller Machine networks. As the network increase it becomes more difficult to supervise it

while the operating costs rise significantly. Cash demand needs to be forecasted accurately so

that banks can avoid storing extra cash money and can profit by mobilizing the idle cash. This

paper is motivated by the Neural Network Association and the NN5 competition. The

objective of the paper is to describe a unique, non-supervising method for forecasting cash

money withdrawals in different ATMs. More precisely, the data consists of 2 years of daily

cash money demand at various ATMs at different randomly selected locations across

England. The only available information is the total cash withdrawals in each ATM at the end

of each day. Having limited domain knowledge and no information on the causal forces we

use wavelet analysis to extract the dynamics of the underlying process of each ATM. Next

wavelet neural networks were used in order to find the true generating process of each ATM

and to forecast the cash money demand up to 56 day ahead. The performance of the proposed

technique is evaluated using various error and fitting criteria.