{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:17:10Z","timestamp":1771337830252,"version":"3.50.1"},"reference-count":43,"publisher":"Emerald","issue":"9","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2022,9,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>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\u2019 cluster where the forecasting results of the aggregated series are appropriate to use.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-03-2021-0235","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T04:13:06Z","timestamp":1625717586000},"page":"2673-2694","source":"Crossref","is-referenced-by-count":5,"title":["An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data"],"prefix":"10.1108","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2397-0010","authenticated-orcid":false,"given":"Michele","family":"Cedolin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5324-0612","authenticated-orcid":false,"given":"Mujde","family":"Erol Genevois","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"key2022090502561129200_ref001","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN.2012.6252476","article-title":"Comparing NARX and NARMAX models using ANN and SVM for cash demand forecasting for ATM","year":"2012"},{"issue":"3","key":"key2022090502561129200_ref002","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.ijforecast.2010.09.005","article-title":"Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition","volume":"27","year":"2011","journal-title":"International Journal of Forecasting"},{"issue":"4","key":"key2022090502561129200_ref003","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.1007\/s13369-018-3647-7","article-title":"The improvement of forecasting ATMS cash demand of Iran banking network using convolutional neural network","volume":"44","year":"2019","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"2","key":"key2022090502561129200_ref004","first-page":"318","article-title":"Approximating methodology: managing cash in automated teller machines using fuzzy ARTMAP network","volume":"3","year":"2014","journal-title":"International Journal of Enhanced Research in Science Technology and Engineering"},{"key":"key2022090502561129200_ref005","first-page":"1","article-title":"A long-short-term-memory based model for predicting ATM replenishment amount","year":"2020"},{"issue":"2","key":"key2022090502561129200_ref006","first-page":"133","article-title":"Cash withdrawals forecasting by neural networks","volume":"3","year":"2011","journal-title":"Journal of Computational Optimization in Economics and Finance"},{"key":"key2022090502561129200_ref007","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/j.1467-9876.2007.00599.x","article-title":"A statistical model for the temporal pattern of individual automated teller machine withdrawals","volume":"57","year":"2008","journal-title":"Journal of the Royal Statistical Society Series C-Applied Statistics"},{"issue":"2","key":"key2022090502561129200_ref008","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1177\/1471082X0801000205","article-title":"Predicting the amount individuals withdraw at cash machines using a random effects multinomial model","volume":"10","year":"2010","journal-title":"Statistical Modelling"},{"issue":"4","key":"key2022090502561129200_ref009","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ijforecast.2009.09.009","article-title":"Predictive-sequential forecasting system development for cash machine stocking","volume":"26","year":"2010","journal-title":"International Journal of Forecasting"},{"key":"key2022090502561129200_ref010","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.procs.2015.07.554","article-title":"Improvement of demand forecasting models with special days","volume":"59","year":"2015","journal-title":"Procedia Computer Science"},{"key":"key2022090502561129200_ref011","first-page":"1","article-title":"ATM cash replenishment under varying population coverage requirements","volume":"1","year":"2020","journal-title":"Journal of the Operational Research Society"},{"key":"key2022090502561129200_ref012","unstructured":"Crone, S. 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