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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 December 2025 | Viewed by 1471

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 (2 papers)

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38 pages, 5792 KiB  
Article
Bibliometric Insights into Time Series Forecasting and AI Research: Growth, Impact, and Future Directions
by Adrian Domenteanu, Paul Diaconu and Camelia Delcea
Appl. Sci. 2025, 15(11), 6221; https://doi.org/10.3390/app15116221 - 31 May 2025
Viewed by 389
Abstract
Considering that nowadays the economy plays a crucial role, time series forecasting has become an essential tool across various economic areas and industries. The process of predicting future trends based on historical values in a reliable and accurate manner has generated numerous benefits, [...] Read more.
Considering that nowadays the economy plays a crucial role, time series forecasting has become an essential tool across various economic areas and industries. The process of predicting future trends based on historical values in a reliable and accurate manner has generated numerous benefits, such as simplified decision-making processes or strategic planning and reduced risk management. Furthermore, with the advancement made through the use of Artificial Intelligence (AI) methods, time series forecasting has quickly become more precise, adaptive, and scalable, being able to better overcome real-world challenges. In this context, the present paper analyzes the implications of artificial intelligence in time series forecasting by evaluating the scientific articles from the field indexed in Clarivate Analytics’ Web of Science Core Collection database. Through a bibliometric approach, the research identifies key journals, affiliations, authors, and countries, as well as the collaboration networks among authors and countries. It also analyzes the most frequently used keywords and authors’ keywords. The annual growth rate of 23.11% indicates sustained interest among researchers. Prominent journals such as IEEE Access, Energies, Mathematics, Applied Sciences—Basel, and Applied Energy have been the home for the most published papers in this field. Further, thanks to the Biblioshiny library in R, a variety of visualizations have been created, including thematic maps, three-field plots, and word clouds. A comprehensive review of the most cited papers has been performed to highlight the role of AI in time series forecasting. Research results and methods confirmed the versatility of the topics, which have been applied in various fields, such as, but not limited to, finance, energy, climate, and healthcare, and are further discussed. Cutting-edge methodologies and approaches that lead to the transformation of the field of time series analysis in the context of AI are uncovered and discussed through the use of thematic maps. Full article
(This article belongs to the Special Issue Advanced Methods for Time Series Forecasting)
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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
Cited by 1 | Viewed by 696
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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