Call for Papers: Advanced Time Series Analysis: Theories, Methods, and Applications

Background

The increasing complexity and scale of time series data have spurred rapid innovations in analytical techniques, especially in the context of big data and artificial intelligence. Traditional approaches often struggle with the challenges posed by large-scale, high-dimensional, and irregularly sampled datasets commonly encountered in areas such as finance, healthcare, industrial automation, and environmental monitoring.

This special session is dedicated to exploring state-of-the-art methodologies in time series analysis, emphasizing robust theoretical foundations, advanced machine learning and deep learning methods, and scalable algorithms. The session seeks to foster interdisciplinary dialogue and collaboration, bridging academia and industry to accelerate the development and adoption of novel analytical techniques that enhance predictive accuracy, anomaly detection capabilities, and risk management in dynamic environments.

Scope

This special track aims to bring together researchers, practitioners, and experts to explore the latest advancements, challenges, and opportunities in this domain by promoting cutting-edge research and innovation in Advanced Time Series Analysis. It aims to facilitate knowledge sharing and cross-disciplinary collaborations, discuss real-world applications and case studies, and identify emerging trends and future directions. We welcome contributions from a wide range of topics, including but not limited to:

• Advanced modeling and forecasting techniques tailored for time series data
• Integration of deep learning, hybrid approaches, and traditional statistical methods
• High-dimensional and multivariate time series analysis
• Robust anomaly detection, risk assessment, and uncertainty quantification
• Techniques for real-time and streaming data analysis
• Spatiotemporal data mining and fusion of temporal with spatial analytics
• Scalable algorithms for processing large-scale time series and sensor data
• Explainable and interpretable time series models
• Transfer learning and domain adaptation techniques in temporal data analysis
• Integration of time series analysis with non-temporal data modalities for enhanced predictive modeling
• Domain-specific applications in finance, healthcare, climate science, cybersecurity, and industrial systems

Formatting Guidelines

• Papers must be written in English and present unpublished contributions relevant to data mining and related fields.
• Manuscripts must adhere strictly to the LNAI (Lecture Notes in Artificial Intelligence) format. Templates and details on the LNCS style can be found on Springer’s Author Instructions webpage.
• Papers should NOT exceed 15 pages in LNAI format.
• All submissions will undergo a double-blind peer review process, meaning:
  • Author identities and affiliations are concealed from reviewers.
  • Submissions must be suitably blinded, omitting author and affiliation details.
  • Accidental identification must be diligently avoided by both authors and reviewers.
  • The author list provided at submission is final and cannot be altered post-submission.
• Failure to adhere to these guidelines may result in desk rejection.

Submission Guidelines

Authors are invited to submit original research papers, case studies, and technical reports aligned with the theme Advanced Time Series Analysis: Theories, Methods, and Applications. Submissions must follow the conference's formatting guidelines and be submitted through the CMT online submission system.

When submitting your manuscript, please select the "Special Session Track" and choose the area "Special Session: Advanced Time Series Analysis."

You can submit your paper at: Submit Your Paper

Important Dates (AoE Time)

Session Chairs