Alliance for Digital health At Monash (ADAM)’s Post

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View profile for Ming Jin, graphic

Assistant Professor at Griffith University

#WebConf2025 📣 Call for AI4TS workshop papers! #CFP Workshop on 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗪𝗲𝗯-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗪𝗪𝗪'𝟮𝟱-𝗔𝗜𝟰𝗧𝗦) ⏰Submission deadline: 18 Dec 2024 🌐 Website: https://lnkd.in/gu2MYRQd This workshop encourages submissions of innovative solutions for a broad range of web-centric time series analysis problems. 📎Topics of interest include but are not limited to📎 ·       Time series forecasting and prediction ·       Spatio-temporal forecasting and prediction ·       Time series anomaly detection and diagnosis ·       Time series change point detection ·       Time series classification and clustering ·       Time series similarity search ·       Time series indexing ·       Time series compression ·       Time series pattern discovery ·       Interpretation and explanation in time series ·       Causal inference in time series ·       Bias and fairness in time series ·       Foundation models for time series ·       Large language models (LLMs) for time series ·       Federated learning and security in time series ·       Benchmarks, experimental evaluation, and comparison for time series analysis tasks ·       Time series applications in various areas: E-commerce, Cloud computing, Transportation, Fintech, Healthcare, Internet of things, Wireless networks, Predictive maintenance, Energy, and Climate, etc. 📣 Workshop Organisers: -Ming Jin, Griffith University -Mahsa Salehi, Monash University -Yuxuan Liang, The Hong Kong University of Science and Technology -Dongjin Song, University of Connecticut -Flora Salim, University of New South Wales -Min Wu, A*STAR -Shirui Pan, Griffith University -Qingsong Wen, Squirrel Ai Learning We look forward to your participation and contribution to this workshop and hope to hear from you soon and meet you in Sydney in 2025 🌊🌉 #WebConf #WebConf2025 #TheWebConf #TheWebConf2025 #workshop #ai #deeplearning #web #webmining #timeseries #timeseriesanalysis #llm #gnn #spatiotemporal #benchmarking #forecasting #classification #anomalydetection #research #innovation

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