MulTiSA 2025

About

Contemporary algorithms for time series management predominantly handle univariate time series. Modern data sources frequently generate richer, multivariate time series. Examples include sensors monitoring multiple variables (e.g., temperature, wind, rainfall), financial time series (bid/ask prices, volume), and data from scientific and medical equipment. Currently, only very few algorithms address the management, analysis, and extraction of insights from such multivariate data. Moreover, existing work is tailored to specific needs. Foundational functionalities that have propelled advancements in univariate time series analysis, e.g., indexing, cannot be trivially extended to the multivariate case. This limitation significantly restricts existing efforts for analyzing multivariate time series.

This workshop will bring together researchers and practitioners working with multivariate time series, to present and discuss open problems and solutions, and to foster collaborations. Industry will participate for presenting requirements and current approaches, and to reach out to the ICDE community. Researchers will share their novel and ongoing work. The full-day workshop will feature: (a) 10 paper presentations (short papers and demos up to 6 pages, long papers up to 12 pages), (b) two invited talks from industry and domain experts, (c) panel discussion, and time for fostering collaborations.

The accepted papers will be included in the ICDEW proceedings.

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Topics of interest

The topics of interest include (but are not limited to):

Open challenges in multivariate time series management
Similarity search on multivariate time series, and detection of multivariate correlations and similarity measures
Online analytical processing for multivariate time series
Streaming and/or distributed analytics on multivariate time series
Storing, indexing, and querying multivariate time series
Sketching and summarizing multivariate time series
Data preparation (data cleaning, noise removal, handling missing values) on multivariate time series
Forecasting and anomaly detection for multivariate time series
Machine learning and deep learning techniques for multivariate time series
Interactive visualization and analytics on (streaming) multivariate time series
Handling uncertainty
Privacy-preserving analytics on multivariate data
Requirements, applications, and query languages for multivariate time series analytics
Foundation models for multivariate time series

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Submission Guidelines

The workshop will accept regular papers (up to 8 pages, excluding references) and short papers describing work in progress, demos, vision/outrageous ideas (up to 4 pages, excluding references). All submissions must be prepared in accordance with the IEEE template available here. The workshop follows the same rules of Conflicts of Interest (COI) as ICDE 2025. The following are the page limits (excluding references):