Purpose

The 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/approach

A 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.

Findings

The 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/implications

The 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 implications

The 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/value

This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

中文翻译：

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采用计量经济学模型的单个时间序列的平均方法：对 NN5 ATM 交易数据的案例研究

目的

研究目标是提高自动柜员机 (ATM) 现金需求预测问题的计算效率。它提出了一个实用的决策过程，该过程使用银行 ATM 网络的聚合时间序列。目的是通过找到适合使用聚合系列预测结果的机器集群来减少单个模型预测的 ATM 数量。

设计/方法/方法

为了通过使用NN5竞赛数据集降低ATM网络的计算复杂度，提出了一种比较统计预测方法。综合自回归移动平均线 (ARIMA) 及其季节性版本 SARIMA 适合每个时间序列。然后，引入平均时间序列以简化对每个 ATM 执行的预测过程。通过计算每台机器的预测精度变化来识别可使用平均序列预测的 ATM。

发现

所提出的方法通过不同的误差指标进行评估，并与文献结果进行比较。结果表明，具有可容忍精度损失的 ATM 可以被视为一个集群，并且可以使用基于聚合序列的单个模型进行预测。

研究限制/影响

该研究基于公共数据集。金融机构不喜欢共享其 ATM 交易数据，因此可访问的数据是有限的。

实际影响

建议的实用方法将有利于金融机构使用，因为它减少了为预测过程分配的计算时间和资源，因为它减少了拥有过多 ATM 的金融机构。

原创性/价值

本研究通过引入聚合时间序列方法，为具有挑战性的现金需求预测过程提供了一种有效的简化方法。