A Method Based on RBF-DDA Neural Networks for Improving Novelty Detection in Time Series

Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. 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. Alternatively, a number of different classification-based techniques have been recently proposed for this problem. The idea of these methods is to learn to classify time series windows as normal or novelty. Unfortunately, in many cases of interest there are only normal data available for training. Several of the classificationbased techniques tackle this problem by adding random negative samples to the training set. In some cases the performance of the novelty detection method depends on the number of random negative samples added and selection of this number can be a problem. In this work, we present a met...

• Type: Conference
• Year: 2004
• Where: FLAIRS
• Authors: Adriano L. I. Oliveira, Fernando Buarque de Lima Neto, Silvio Romero de Lemos Meira
• Keywords: Artificial Intelligence, FLAIRS 2004, Negative Samples, Time Series, Time Series Forecasting