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Leadership discovery when data correlatively evolve

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Abstract

Nowadays, World Wide Web is full of rich information, including text data, XML data, multimedia data, time series data, etc. The web is usually represented as a large graph and PageRank is computed to rank the importance of web pages. In this paper, we study the problem of ranking evolving time series and discovering leaders from them by analyzing lead-lag relations. A time series is considered to be one of the leaders if its rise or fall impacts the behavior of many other time series. At each time point, we compute the lagged correlation between each pair of time series and model them in a graph. Then, the leadership rank is computed from the graph, which brings order to time series. Based on the leadership ranking, the leaders of time series are extracted. However, the problem poses great challenges since the dynamic nature of time series results in a highly evolving graph, in which the relationships between time series are modeled. We propose an efficient algorithm which is able to track the lagged correlation and compute the leaders incrementally, while still achieving good accuracy. Our experiments on real weather science data and stock data show that our algorithm is able to compute time series leaders efficiently in a real-time manner and the detected leaders demonstrate high predictive power on the event of general time series entities, which can enlighten both weather monitoring and financial risk control.

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Di Wu, Yiping Ke & Jeffrey Xu Yu

  2. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

  3. The Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Lei Chen

Authors
  1. Di Wu
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  2. Yiping Ke
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  3. Jeffrey Xu Yu
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  4. Philip S. Yu
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  5. Lei Chen
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Correspondence to Yiping Ke.

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Wu, D., Ke, Y., Yu, J.X. et al. Leadership discovery when data correlatively evolve. World Wide Web 14, 1–25 (2011). https://doi.org/10.1007/s11280-010-0095-z

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  • DOI: https://doi.org/10.1007/s11280-010-0095-z

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