An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

PurposeThe research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.Design/methodology/approachA comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.FindingsThe proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.Research limitations/implicationsThe research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.Practical implicationsThe proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.Originality/valueThis study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

Corporate Money Demand

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Money Leaks in Banking ATM’s Cash-Management Systems

Some widely-accepted practices on banking ATM networks may negatively affect an efficient liquidity management. This paper analyses ATM cash management in light of empirical evidence which suggests that banking ATMs tend to be overloaded beyond the customer’s needs. This, in turn, results in high opportunity costs. While this is not perceived by banks as particularly harmful, it might have a damaging impact on other business which revolves exclusively around ATM networks, such as cashback sites. A dormant money case may be solved­­ by an appropriate tool matching the ATM’s cash to the user’s needs. Supported by a large database of banking records, this paper also provides model validation for a set of theorems previously developed by the author, resulting here in a cutting-edge, reliable forecasting system, suitable for anticipating ATMs cash demand as well as coupling with other supply chain planning processes.