@misc{Jasiński_Tomasz_Modelling_2019,
 author={Jasiński, Tomasz and Bochenek, Anna},
 identifier={DOI: 10.15611/ie.2019.2.04},
 year={2019},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Informatyka Ekonomiczna = Business Informatics, 2019, Nr 2 (52), s. 58-68},
 language={eng},
 abstract={The study presents the construction process of a model that forecasts arrears in dwelling payments in individual municipalities in Poland depending on the values of economic data from previous years. This enables to estimate arrears that will arise in the investigated municipalities in the year of analysis. The model constructed on the basis of artificial neural networks, which is a tool from the area of artificial intelligence, was used to carry out forecasts. More than one hundred thousand networks with multilayer perceptron (MLP) and radial basis function (RBF) architectures were tested. The MAPE for prediction of the number of indebted dwellings in municipalities with at least 50 indebted premises was 6.08%. The correctness of forecasts in the area of the direction of changes of household debt in municipalities between 2014 and 2015 was 76.84%},
 title={Modelling of arrears in payments for dwelling using artificial neural networks},
 type={artykuł},
 keywords={payment arrears, household debt, forecast, municipalities, artificial neural networks, zaległości płatnicze, zadłużenie gospodarstw domowych, prognozy, gminy, sztuczne sieci neuronowe},
}