Fast Burst Correlation of Financial Data

We examine the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. Our methodology is comprised of two steps: a burst detection part, followed by a burst indexing step. The burst detection scheme imposes a variable threshold on the examined data and takes advantage of the skewed distribution that is typically encountered in many applications. The indexing step utilizes a memory-based interval index for effectively identifying the overlapping burst regions. While the focus of this work is on financial data, the proposed methods and data-structures can find applications for anomaly or novelty detection in telecommunications and network traffic, as well as in medical data. Finally, we manifest the real-time response of our burst indexing technique, and demonstrate the usefulness of the approach for correlating surprising volume trading events at the NY stock exchange.