Detection of abnormal behavior in trade data using Wavelets, Kalman Filter and Forward Search

In this paper we address the issue of the automatic detection of abnormal behavior in time series extracted from international trade data. We motivate, review and use three specific methods, based on solid frameworks: Wavelets, Kalman Filter and Forward Search. These methods have been successfully applied to an important EU policy issue: the analysis of trade data for antifraud and antimoney-laundering, fields in which specialists are often confronted with massive datasets. Our contribution consists in an in-depth study of these approaches to assess their performance, qualitatively and quantitatively. On the one hand, we present these three approaches, underline their specific aspects and detail the used algorithms. On the other hand, we put forward a rigorous assessment methodology. We use this methodology to evaluate each method and also to compare them, on simulated time series and also on real datasets. Results show each method has its specific advantages. Their joint use could be of a high operational impact for our applications, to deal with the variety of patterns occurring in trade data.JRC.G.2-Global security and crisis managemen

Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms

An automated subpixel target detection system has been designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method (discrete wavelet transform) to extract an energy feature vector from each input pixel signature. The dimensionality of the feature vector is reduced to a one-dimensional feature scalar through the process of linear discriminant analysis. Signature classification is determined by nearest mean criterion that is used to assign each input signature to one of two classes, no target present or target present. Classification accuracy ranged from nearly 60% with target SNR at -95dB without any a priori knowledge of the target, to 100% with target SNR at -50dB and a priori knowledge about the location of the target within the spectral bands of the signature

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture