Maier et al. examine artificial neural networks (ANNs) in this paper, aiming to clarify misconceptions and outline best practices for their application in prediction and forecasting.

The authors first address myths about ANNs, such as the perception of them as black boxes and the belief that they require large datasets to be effective. They argue that ANNs operate on well-defined mathematical principles, similar to other modeling techniques, and can perform well even with incomplete data.

The paper details a structured approach to ANN development, including data preprocessing, input selection, data splitting, model architecture selection, model calibration, and validation. The authors emphasize the importance of selecting relevant input variables and appropriate data splitting methods to avoid overfitting and ensure robust model performance. They also discuss validation techniques, such as replicative, predictive, and structural validity checks, to ensure model reliability.

The authors demonstrate the effectiveness of their methodology through case studies and examples. They show how ANNs can be applied to various prediction and forecasting tasks, with improved performance over traditional methods. The results highlight the ability of ANNs to generalize well across different datasets and conditions.

The paper’s strengths:

• Comprehensive clarification: the paper effectively demystifies ANNs by explaining their operational principles and addressing common misconceptions.
• Methodical approach: the detailed methodology for ANN development ensures rigorous and reliable model construction.
• Practical examples: the inclusion of case studies provides practical insights into the application of ANNs, reinforcing the theoretical discussions.

Readers should also be aware of some possible weaknesses:

• Computational demands: the computational requirements for training ANNs can be significant, which may limit their accessibility for some users.
• Applicability scope: the paper focuses primarily on prediction and forecasting, potentially limiting its relevance to other ANN applications.

Maier et al.’s paper is a significant contribution to the field, clarifying the practical aspects of using ANNs for prediction and forecasting. The structured methodology and practical examples provide a valuable guide for both novices and experienced practitioners. Addressing the computational demands and exploring a broader range of applications could further enhance the paper’s impact.