{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:51:00Z","timestamp":1774425060448,"version":"3.50.1"},"posted":{"date-parts":[[2026,3,24]]},"group-title":"Computer Science and Mathematics","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,20]]},"abstract":"<jats:p>This study presents an approach to forecasting water consumption using the ARIMA (Autoregressive Integrated Moving Average) method, with an additional comparison to the Holt- Winters method [1], [ 2 ], [ 3], [ 4], [ 5 ]. The work was based on a set of historical data representing the monthly water consumption of a specific area in the parish of Cambra, municipality of Vouzela, Portugal, covering a period of five years (2018-2022). Initially, the natural logarithmic transformation was applied to normalise the data [ 6], followed by the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test to check the stationarity of the time series [ 7]. Differentiation was applied to achieve the necessary stationarity. The Auto-ARIMA method was used to determine the optimal parameters (p,d,q) based on the Akaike Information Criterion (AIC) [ 8], [9]. In addition, the Holt-Winters method was implemented directly, taking advantage of its ability to deal with non-stationary and non-normally distributed series. This method was applied with additive components and Box-Cox transformation [10 ], automatically incorporating the transformation and adjustment processes for seasonality and trend. Both methods were used to forecast water consumption for the 12 months to 2023. After applying Auto-ARIMA, the series was reversed, i.e. differentiated, and exponentially transformed to return to the original values. The performance of both methods was assessed comparatively, using the Mean Absolute Error as a metric [ 11 ], [12]. This study contributes to the efficient management of water resources by providing a robust methodology for forecasting water consumption, with an emphasis on the detailed application of ARIMA and a complementary comparison with Holt-Winters. Throughout this study, both ARIMA and Holt-Winters will be approached as statistical methods that generate models for forecasting data.<\/jats:p>","DOI":"10.20944\/preprints202603.1851.v1","type":"posted-content","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:03:14Z","timestamp":1774418594000},"source":"Crossref","is-referenced-by-count":0,"title":["Water Consumption Forecasting Using ARIMA and Holt-Winters Methods: A Case Study in Vouzela, Portugal"],"prefix":"10.20944","author":[{"given":"J\u00falio","family":"Rocha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-5706","authenticated-orcid":false,"given":"Salviano","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-1298","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Valente","sequence":"additional","affiliation":[]},{"given":"Filipe Cabral","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"1968","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:05:15Z","timestamp":1774418715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202603.1851\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202603.1851.v1","relation":{},"subject":[],"published":{"date-parts":[[2026,3,24]]},"subtype":"preprint"}}