Application of Time Series Analysis and Forecasting in Computer Science, 2nd Edition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 761
Special Issue Editors
Interests: spatio-temporal data modelling; time series forecasting; pedestrian trajectory prediction
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; machine learning; object detection; visual tracking; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Time series analysis and forecasting have become increasingly vital in recent years due to the proliferation of electronics, the Internet of Things (IoT), and advanced smart sensor technologies. These advancements have enabled the collection of vast amounts of time series data across diverse domains such as finance, energy, healthcare, and environmental monitoring.
Machine learning and deep learning continue to serve as foundational tools for time series analysis and forecasting, excelling in handling complex and high-dimensional data. Recent breakthroughs in large language models (LLMs) and time series foundation models are revolutionizing the field. LLMs have been leveraged to enable natural language-driven time series queries and facilitate question-answering systems for temporal data. Similarly, time series foundation models are emerging as powerful tools, offering pre-trained, transferable representations that significantly enhance performance across various tasks and domains, while reducing the need for task-specific training data.
This Special Issue aims to bring together researchers and practitioners working on novel methods and techniques for time series analysis and forecasting. It welcomes contributions across various application domains, including intelligent transportation, geographic information systems, economics, finance, and environmental science. We particularly encourage submissions that highlight cutting-edge advancements in LLM-driven time series analysis, time series foundation models, and their applications in solving real-world problems in these domains.
Dr. Hao Xue
Dr. Du Huynh
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. Electronics 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
- time series forecasting
- deep learning
- probabilistic forecasting
- forecasting applications
- temporal data modeling
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