Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
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Novelty detection is an important problem in many application fields, such as scene analysis, machine failure detection, and auditing. There has been an increasing interest in time series novelty detection with novel techniques recently developed. An approach to this problem uses time series forecasting by neural networks. However, time series forecasting is a difficult problem, thus, the use of this technique for time series novelty detection is sometimes criticized. Moreover, the short length of the time series available in several important problems makes forecasting an even harder problem. This is the case of some important auditing problems such as accountancy auditing and payroll auditing. In this work we propose a classification-based approach for short time series novelty detection. The idea is to increase the number of patterns of data sets by adding both normal and novelty random patterns. The proposed approach has been evaluated on four real world time series and has shown promising results.