The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Local Correlation Tracking in Time Series
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Leadership discovery when data correlatively evolve
World Wide Web
Discovering shakers from evolving entities via cascading graph inference
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.