Time Series Analysis in the Web: Recent Advances and Future Trends

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.

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Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3715853
Citations
1
Primary Topic
Time Series Analysis and Forecasting
Type
article
Field-Weighted Citation Impact
1.49
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Time Series Analysis in the Web: Recent Advances and Future Trends

Hao Xue, Yuxuan Liang, Flora Dilys Salim, Wei Jin et al.
1 citations
Time Series Analysis and Forecasting
1.49
article

Time Series Analysis in the Web: Recent Advances and Future Trends

Hao Xue, Yuxuan Liang, Flora Dilys Salim, Wei Jin, Ming Jin
article en
1 citations

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.

Griffith University (AU), Emory University (US), UNSW Sydney (AU)
Openalex Percentile: Top 19%
Time Series Analysis and Forecasting
1.49
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