{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T23:46:28Z","timestamp":1725666388466},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,23]]},"abstract":"<jats:p>The paper discusses the study to develop and tune parameters of a nonlinear autoregressive neural network (NARNet or NARNN) model for predicting the number of housing units. Predictions were made for the housing construction market in Poland, which is a dynamically growing European market. Three stages of the housing construction process have been taken into considerationThe paper discusses the study to develop and tune parameters of a nonlinear autoregressive neural network (NARNet or NARNN) model for predicting the number of housing units. Predictions were made for the housing construction market in Poland, which is a dynamically growing European market. Three stages of the housing construction process have been taken into consideration: permits issued for house construction, houses under construction, and completed new houses. Experimental results have shown that a NARNet model can be a very effective tool in the considered scenario. A network model using the Levenberg-Marquardt backpropagation training function achieved the best model fit, as well as the most accurate one-month predictions.<\/jats:p>","DOI":"10.7148\/2023-0284","type":"proceedings-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T07:46:57Z","timestamp":1692172017000},"page":"284-290","source":"Crossref","is-referenced-by-count":0,"title":["Time Series Prediction For The Housing Construction Market With The Use Of NARNet"],"prefix":"10.7148","author":[{"given":"Daria","family":"Wotzka","sequence":"first","affiliation":[]},{"given":"Grazyna","family":"Suchacka","sequence":"additional","affiliation":[]},{"given":"Lukasz","family":"Mach","sequence":"additional","affiliation":[]},{"given":"Pawel","family":"Fracz","sequence":"additional","affiliation":[]},{"given":"Joachim","family":"Foltys","sequence":"additional","affiliation":[]},{"given":"Ionela","family":"Maniu","sequence":"additional","affiliation":[]}],"member":"4144","published-online":{"date-parts":[[2023,6,23]]},"event":{"name":"37th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2023 Proceedings edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni"],"original-title":[],"deposited":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T07:47:13Z","timestamp":1692172033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.scs-europe.net\/dlib\/2023\/2023-0284.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2023-0284","relation":{},"subject":[],"published":{"date-parts":[[2023,6,23]]}}}