Year: 2025
Type: conference-paper
Abstract: Time series analysis has become instrumental in tackling key challenges in web-based applications, such as server load balancing, anomaly detection in e-commerce traffic, and tourism demand forecasting. This proposal introduces a comprehensive half-day lecture-style tutorial for The Web Conference 2025, tailored to professionals, researchers, and practitioners aiming to harness machine learning for analyzing web-sourced time series data. The tutorial will cover foundational principles, data processing techniques, and advanced modeling strategies, equipping attendees with both theoretical understanding and practical skills. Participants will also explore best practices for integrating time series analysis into web-centric workflows, with diverse applications in e-commerce, digital health, and transportation. Led by leading experts in the field, this tutorial provides an invaluable opportunity to deepen knowledge, gain hands-on experience, and foster meaningful connections-bridging the gap between theory and real-world implementation.
Authors: Ming Jin, Wei Jin, Hao Xue, Flora Salim, Yuxuan Liang
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410636897