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 17
Special Issue Editors
Interests: economic cybernetics; consumer behavior; agent-based modeling; sustainable development; gray systems theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals
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
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Keywords
- time series analysis
- complex data
- AI
- hybrid models
- data analysis
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