The Workshop of Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS): Theory, Algorithms, and Applications
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Author(s)
Salehi, Mahsa
Liang, Yuxuan
Song, Dongjin
Salim, Flora
Wu, Min
Pan, Shirui
Wen, Qingsong
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Sydney, NSW, Australia
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Abstract
The Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS) Workshop at The Web Conference 2025 aims to unite experts from academia and industry to advance artificial intelligence for web-centric time series analysis. This full-day workshop will feature at least five keynote talks, paper presentations, and a panel discussion, engaging attendees in topics that span theory, algorithms, and applications within web-based time series analysis. With a focus on web-sourced data from IoT devices, social media, and cloud platforms, the workshop addresses pressing challenges such as anomaly detection, forecasting, classification, and generation across diverse fields, including finance, healthcare, transportation, and biological sciences. AI researchers, practitioners, and policymakers are anticipated to join, fostering a collaborative environment to explore innovative approaches in time series for web applications. Building on the success of prior AI4TS workshops, this event will continue to honor outstanding research contributions. Supported by organization and committee members from both industry and academia, this will mark a new instance of the AI4TS series, following previous workshops hosted with KDD, ICDM, AAAI, IJCAI, etc., each drawing over 80 participants on average.
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WWW '25: Companion Proceedings of the ACM on Web Conference 2025
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Software and application security
Distributed systems and algorithms
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Jin, M; Salehi, M; Liang, Y; Song, D; Salim, F; Wu, M; Pan, S; Wen, Q, The Workshop of Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS): Theory, Algorithms, and Applications, WWW '25: Companion Proceedings of the ACM on Web Conference 2025, 2025, pp. 2203-2206