Innovations and Applications of Time Series Processing

Dear Colleagues,

Time series data permeates critical domains, including finance, healthcare, climate science, engineering, and cybersecurity. With the exponential growth of sensing technologies and large-scale data infrastructures, extracting actionable insights from temporal data has become both crucial and challenging.

This Special Issue seeks contributions that address key challenges in modeling, forecasting, representation learning, anomaly detection, and pattern discovery in time series data. We particularly encourage work that combines deep learning, probabilistic reasoning, signal processing, or hybrid methods to advance both theoretical understanding and practical performance. Contributions tackling real-world complexities—such as multivariate, irregular, or incomplete time series—are especially welcome.

We invite researchers, practitioners, and industry experts to submit original research, review articles, or case studies that advance the field. This Issue aims to serve as a comprehensive resource connecting foundational research with domain-specific applications and fostering innovation and dialogue within the global time series community.

Dr. Emadeldeen Eldele
Dr. Mohamed Ragab
Guest Editors

Manuscript Submission Information

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