 Comparison between artificial neural network and linear model prediction performance for FDI disparity and the growth rate of companies in Hungarian countiesDevesh Singh International Journal of Business Information Systems, 2023, vol. 43, issue 4, 542-552 Abstract: Artificial neural network (ANN) is a growing concept in economics. The objective of this article is to compare the prediction performance of FDI inflow in companies and the growth rate of the companies in Hungarian counties through linear and ANN model. This article examines the regional level data of GDP per capita, urbanisation, market share of foreign investment received companies, labour productivity and agglomeration over the period of 2001 to 2018. Further, this article used the RStudio platform to analyse the data with resilient backpropagation with weight backtracking algorithm and root mean square error to check the predictability performance between ANN and linear model. The result suggests that ANN has an ability to converge, generalised and learns the growth rate of companies and FDI disparities for forecasting. The scatter plot of performance comparison depicts, ANN framework has better efficiency of forecast compared to the linear model. Keywords: artificial neural networks; ANNs; forecasting; linear model; FDI; business information system; growth rate; prediction performance; machine learning; Hungary. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:43:y:2023:i:4:p:542-552 Access Statistics for this article More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd |
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