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

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....

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Bibliographic Details
Published in:Algorithms 2019-04, Vol.12 (4), p.85
Main Author: Livieris, Ioannis E.
Format: Article
Language:English
Subjects:
Algorithms
artificial neural networks
Classification
constrained optimization
Credit scoring
Datasets
Decision making
economic data mining
Economic forecasting
limited memory BFGS
Machine learning
Model accuracy
Neural networks
Recurrent neural networks
Weight
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Summary: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.
ISSN:1999-4893
1999-4893
DOI:10.3390/a12040085