Computer Science – Learning
Scientific paper
2009-05-21
Machine Learning 74:281,2009
Computer Science
Learning
Scientific paper
10.1007/s10994-008-5093-3
Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD's reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.
Alcock Charles
Brodley Carla E.
Protopapas Pavlos
Rebbapragada Umaa
No associations
LandOfFree
Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-444640