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Mathematics
Special Issues
Statistical Methods for Forecasting and Risk Analysis
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Statistical Methods for Forecasting and Risk Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 824

Special Issue Editors

Prof. Marialuisa Restaino

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Guest Editor
Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy
Interests: discrete choice models; regression modeling; survival analysis; imbalanced datasets
Special Issues, Collections and Topics in MDPI journals
Prof. Marcella Niglio

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Guest Editor
Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy
Interests: regression modeling; imbalanced datasets; variable selection; time series models
Prof. Emanuela Raffinetti

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Guest Editor
Department of Economics and Management, University of Pavia, Via San Felice al Monastero 5, 27100 Pavia, Italy
Interests: SAFE (sustainable, accurate, fair and explainable) artificial intelligence methods; machine learning model validation methods; assessment of operational and cyber risks; dependence analysis; sub-sampling methods; inequality measures for income distributions

Special Issue Information

Dear Colleagues,

In recent years, the fast accumulation and growing availability of data and computational power have led to the spread of sophisticated statistical and machine learning models, together with artificial intelligence-based systems.

While these methods have gained great popularity in data science applications, due to their capability of giving rise to powerful predictions, they have an intrinsic black box nature, where input data are elaborated through complex processes without effective control and monitoring of the operational, reputational, and strategic risks emerging from potential biases in forecasting.

Therefore, the use of statistical methods to predict financial, default, funding loss, and diagnostic-related risks (among others) requires the main criteria supporting the predictions to be known for assessing the related severity and fostering ad hoc measures to reduce them in the presence of shocks.

This Special Issue aims to share recent research, stimulate discussion, and disclose new research directions regarding the use of statistical techniques and AI systems to address the challenges which arise in forecasting and risk analysis. A particular focus will be devoted to papers (theoretical, empirical, and review) drawing attention to the application of models, computational techniques in estimation, simulation, and prediction in risk analysis contributing to decision making in various contexts (banks, firms, industries, transports, environmental and healthy institutions, higher education institutions, etc.).

Prof. Marialuisa Restaino
Prof. Marcella Niglio
Prof. Emanuela Raffinetti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk analysis
  • forecasting and accuracy
  • statistical modeling
  • model selection
  • applied statistics
  • machine learning
  • artificial intelligence
  • massive dataset
  • high (and ultra-high)-dimensional dataset
  • variable selection

Published Papers (1 paper)

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Research

18 pages, 6319 KiB  
Article
Back to Basics: The Power of the Multilayer Perceptron in Financial Time Series Forecasting
by Ana Lazcano, Miguel A. Jaramillo-Morán and Julio E. Sandubete
Mathematics 2024, 12(12), 1920; https://doi.org/10.3390/math12121920 - 20 Jun 2024
Viewed by 419
Abstract
The economic time series prediction literature has seen an increase in research leveraging artificial neural networks (ANNs), particularly the multilayer perceptron (MLP) and, more recently, transformer networks. These ANN models have shown superior accuracy compared to traditional techniques such as autoregressive integrated moving [...] Read more.
The economic time series prediction literature has seen an increase in research leveraging artificial neural networks (ANNs), particularly the multilayer perceptron (MLP) and, more recently, transformer networks. These ANN models have shown superior accuracy compared to traditional techniques such as autoregressive integrated moving average (ARIMA) models. The most recent models in the prediction of this type of neural network, such as recurrent or Transformers models, are composed of complex architectures that require sufficient processing capacity to address the problems, while MLP is based on densely connected layers and supervised learning. A deep understanding of the limitations is necessary to appropriately choose the ideal model for each of the prediction tasks. In this article, we show how a simple architecture such as the MLP allows a better adjustment than other models, including a shorter prediction time. This research is based on the premise that the use of the most recent models will not always allow better results. Full article
(This article belongs to the Special Issue Statistical Methods for Forecasting and Risk Analysis)
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