Special Issue: Advances in Time Series Forecasting
Guest Editors
Prof. Dr. Francisco Martínez-Álvarez (Leading Guest Editor)
Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
Email: fmaralv@upo.es
Prof. Jose. F. Torres
Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
Email: jftormal@upo.es
Prof. María Martínez-Ballesteros
Department of Computer Science, University of Seville, ES-41012 Seville, Spain
Email: mariamartinez@us.es
Prof. Alicia Troncoso
Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
Email: atrolor@upo.es
Manuscript Topics
Time series forecasting plays a critical role in various domains, including finance, economics, environmental science, healthcare, and many others. With the increasing availability of data and the advancements in machine learning and statistical methods, time series forecasting has witnessed a significant evolution. This special issue aims to bring together the latest advances, innovations, and research in the field of time series forecasting.
We invite researchers, academicians, and professionals to submit original research papers and case studies on topics including but not limited to:
• Advanced machine learning and deep learning techniques for time series forecasting.
• Multivariate time series analysis and forecasting.
• Forecast evaluation metrics and benchmarking.
• Forecasting for anomaly detection and event prediction.
• Big data and high-frequency time series forecasting.
• Time series forecasting for environmental and climate data.
• Real-time and online forecasting methods.
• Applications of time series forecasting in industry and government.
Instruction for Authors
http://www.aimspress.com/math/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/
