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Applied Sciences
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Advanced Methods for Time Series Forecasting
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Advanced Methods for Time Series Forecasting

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 484

Special Issue Editors

Dr. Camelia Delcea

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: economic cybernetics; consumer behavior; agent-based modeling; sustainable development; gray systems theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nora Monica Chirita

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: economic cybernetics; consumer behavior; systems analysis; systems diagnosis; dynamics; sustainable development; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s world, which is highly driven by data, time series forecasting has become an essential tool across numerous industries. The process of accurately predicting future trends based on historical data has become crucial for decision-making processes, risk management, and planning in general. As technology continues to advance, the methods for analyzing and forecasting time series data continue to follow these advancements steps by becoming more precise, adaptive, and scalable in the face of real-world challenges.

The present Special Issue on "Advanced Methods for Time Series Forecasting" aims to explore cutting-edge methodologies and approaches that are transforming the field of time series analysis. With the advancement of artificial intelligence and the rise of hybrid models, a variety of options for handling complex data have become available, which have the potential to capture intricate patterns, nonlinear relationships, and long-term dependencies that traditional methods might overlook.

We welcome submissions from both theoretical and applied perspectives, including empirical research, case studies, comparative analyses, and reviews.

We look forward to receiving your contributions.

Dr. Camelia Delcea
Prof. Dr. Nora Monica Chirita
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. Applied Sciences 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 2400 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

  • time series analysis
  • complex data
  • AI
  • hybrid models
  • data analysis

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Published Papers (1 paper)

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Research

20 pages, 9086 KiB  
Article
Monte Carlo Dropout Neural Networks for Forecasting Sinusoidal Time Series: Performance Evaluation and Uncertainty Quantification
by Unyamanee Kummaraka and Patchanok Srisuradetchai
Appl. Sci. 2025, 15(8), 4363; https://doi.org/10.3390/app15084363 - 15 Apr 2025
Viewed by 182
Abstract
Accurately forecasting sinusoidal time series is essential in various scientific and engineering applications. However, traditional models such as the seasonal autoregressive integrated moving average (SARIMA) rely on assumptions of linearity and stationarity, which may not adequately capture the complex periodic behaviors of sinusoidal [...] Read more.
Accurately forecasting sinusoidal time series is essential in various scientific and engineering applications. However, traditional models such as the seasonal autoregressive integrated moving average (SARIMA) rely on assumptions of linearity and stationarity, which may not adequately capture the complex periodic behaviors of sinusoidal data, including varying amplitudes, phase shifts, and nonlinear trends. This study investigates Monte Carlo dropout neural networks (MCDO NNs) as an alternative approach for both forecasting and uncertainty quantification. The performance of MCDO NNs is evaluated across six sinusoidal time series models, each exhibiting different dynamic characteristics. Results indicate that MCDO NNs consistently outperform SARIMA in terms of root mean square error, mean absolute percentage error, and the coefficient of determination, while also producing more reliable prediction intervals. To assess real-world applicability, the airline passenger dataset is used, demonstrating MCDO’s ability to effectively capture periodic structures. These findings suggest that MCDO NNs provide a robust alternative to SARIMA for sinusoidal time series forecasting, offering both improved accuracy and well-calibrated uncertainty estimates. Full article
(This article belongs to the Special Issue Advanced Methods for Time Series Forecasting)
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