Event Discovery in Time Series
People:
Carla Brodley, Dae-Won Kim, Rosanne Di Stefano, Dan Preston, Pavlos Protopapas.The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient's electrocardiogram. Current methods for identifying events require a fixed sliding window size, which is not ideal for all applications and could overlook important events. In this work, we develop probability models for finding the significance of an arbitrary-sized sliding window, and use these probabilities to find areas of significance. Because a brute force search of all sliding windows of all window sizes would be computationally intractable, we introduce a method for quickly approximating the results. We apply our method to our motivating domain of astronomy by analyzing over 100,000 time series from the MACHO survey, in which 56 different sections of the sky are considered, each with one or more known events. Our method was able to recover 100% of these events in the top 1% of the results, essentially pruning 99% of the data. Interestingly, our method was able to identify events that do not pass traditional event discovery procedures.
TAOS, MACHO.
Publications:
No publications are listed for this project.
Last Updated at: 2008-09-24 03:42 PM.