Journal
NEUROCOMPUTING
Volume 70, Issue 1-3, Pages 79-92Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2006.05.008
Keywords
time series; novelty detection; fraud detection; anomaly detection; forecasting; confidence intervals; neural network
Categories
Ask authors/readers for more resources
Novelty detection in time series is an important problem with application in a number of different domains such as machine failure detection and fraud detection in financial systems. One of the methods for detecting novelties in time series consists of building a forecasting model that is later used to predict future values. Novelties are assumed to take place if the difference between predicted and observed values is above a certain threshold. The problem with this method concerns the definition of a suitable value for the threshold. This paper proposes a method based on forecasting with robust confidence intervals for defining the thresholds for detecting novelties. Experiments with six real-world time series are reported and the results show that the method is able to correctly define the thresholds for novelty detection. (c) 2006 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available