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B. Eddy Patuwo
Researcher at Saint Petersburg State University
Publications - 21
Citations - 5695
B. Eddy Patuwo is an academic researcher from Saint Petersburg State University. The author has contributed to research in topics: Artificial neural network & Inventory control. The author has an hindex of 13, co-authored 21 publications receiving 5225 citations. Previous affiliations of B. Eddy Patuwo include College of Business Administration & Kent State University.
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Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
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Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
TL;DR: In this article, the authors present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction and demonstrate the link between neural networks and traditional Bayesian classification theory.
Theory and Methodology Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
TL;DR: A comprehensive review of neural network applications in this area is given and the link between neural networks and traditional Bayesian classification theory is illustrated, indicating that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation.
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A simulation study of artificial neural networks for nonlinear time-series forecasting
TL;DR: Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task.
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A Cross‐Validation Analysis of Neural Network Out‐of‐Sample Performance in Exchange Rate Forecasting
TL;DR: In this paper, the authors investigate the potentials of neural network models by employing two cross-validation schemes and show that neural networks are a more robust forecasting method than the random walk model.