New Deep Learning Approach for Time Series Forecasting, 2nd Edition
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 856
Special Issue Editors
Interests: deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; kernel methods; lustering; intrinsic dimension estimation; gesture recognition; handwriting recognition; time series prediction; dimensionality reduction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will focus on the output of solar power plants, air temperature, and more. Due to the rapid innovation in sensor technology, the amount of collected time series data is growing exponentially. In various real-world scenarios, managers urgently need to utilize this large amount of time series data for short-term scheduling or advance planning. As a result, researchers worldwide are focusing on developing accurate time series forecasting methods to help plan ahead, save resources, and avoid undesired scenarios.
In recent years, with the development of deep learning methods, neural networks such as the Temporal Convolutional Neural Network (TCN) and Transformer have demonstrated outstanding performance in various time series forecasting tasks, including traffic flow forecasting, photovoltaic power forecasting, and electricity load forecasting. Compared to traditional time series methods, deep learning methods offer the advantages of high accuracy, robustness, and wide applicability in time series forecasting. Moreover, deep learning methods can handle larger-scale time series data, adapting to the significant growth in the volume of time series data. Hence, mining outstanding neural network models is of great importance for the development of the time series forecasting field.
This Special Issue aims to collect high-quality research articles written by experts that concentrate on the tasks of applying deep learning methods in time series forecasting. The mission is to promote the improvement of the accuracy of existing time series prediction tasks, explore more meaningful time series prediction tasks, and provide more accurate and scientific guidance for realistic tasks.
Dr. Binbin Yong
Dr. Francesco Camastra
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. Information is an international peer-reviewed open access monthly 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 1800 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 forecasting
- deep learning
- spatio-temporal forecasting
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