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Novelty, or abnormality, detection aims to identify patterns within data streams that do not conform to expected behaviour. This paper introduces a novelty detection technique using a combination of Gaussian Processes and extreme value theory to identify anomalous behaviour in streaming data. The proposed combination of continuous and count stochastic processes is a principled approach towards dynamic extreme value modelling that accounts for the dynamics in the time series, the streaming nature of its observation as well as its sampling process. The approach is tested on both synthetic and real data, showing itself to be effective in our primary application of maritime vessel track analysis. © 2012 IEEE.

Much of the early work on data mining concentrated on developing algorithms that focused on classification accuracy. A more challenging and practical problem is to devise algorithms that learn rules or associations that optimize income and take better account of costs. Thus, for example, in marketing, we are interested in identifying investments that produce greatest income given the cost of marketing. In medical diagnosis we need to take account of the costs of tests required in diagnosis, such as blood tests, x-rays and scans. This is an important and growing research area, which has produced interesting work in such areas as associative rule mining, clustering and classification using a variety of different data mining techniques and is used in a wide range of application domains such as business, drug design, cosmetic industry, food industry, pollution detection and financial crisis prediction.

In this paper, we propose a method that extracts information from sequences of financial ratios and investigate the usefulness of this information for bankruptcy prediction, which constitutes an important class of financial services. We use the annual financial reports available from an external financial information services provider to extract predictors based on the Markov for Discrimination (MFD) methodology. These predictors are used as inputs in a binary classification model, which applies logistic regression to estimate the odds of bankruptcy. The results suggest that MFD-based predictors can achieve substantial predictive performance in terms of the AUC and the 5-percent predictive lift, which are two relevant performance metrics in our case.