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Open AccessArticle

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Department of Computer & Informatics Engineering, Technological Educational Institute of Western Greece, GR 263-34 Antirrio, Greece
Algorithms 2019, 12(4), 85; https://doi.org/10.3390/a12040085
Received: 28 February 2019 / Revised: 16 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
(This article belongs to the Special Issue Mining Humanistic Data 2019)
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Abstract

During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning. View Full-Text
Keywords: artificial neural networks; machine learning; economic data mining; classification; constrained optimization; limited memory BFGS artificial neural networks; machine learning; economic data mining; classification; constrained optimization; limited memory BFGS
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Livieris, I.E. Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks. Algorithms 2019, 12, 85.

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