Detecting Leaders from Correlated Time Series

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Analyzing the relationships of time series is an important problem for many applications, including climate monitoring, stock investment, traffic control, etc. Existing research mainly focuses on studying the relationship between a pair of time series. In this paper, we study the problem of discovering leaders among a set of time series 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 as time goes by, since the dynamic nature of time series results in highly evolving relationships between time series. 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 climate 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 climate monitoring and financial risk control.