Leadership discovery when data correlatively evolve |
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Authors: | Di Wu Yiping Ke Jeffrey Xu Yu Philip S Yu Lei Chen |
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Affiliation: | (1) Scientific Instrument Manufacturer Bruker BioSpin MRI GmbH, Rudolf-Plank-Str. 23, 76275 Ettlingen, Germany;(2) Institute for Computer Engineering in Mannheim, University of Heidelberg, B6, 23–29B, 68131 Mannheim, Germany |
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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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