Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...

Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic models that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that oper...

To implement a mobile radio system, wave propagation models are necessary to determine propagation characteristics for any arbitrary installation. The predictions are required for a proper coverage planning, the determination of multipath effects as well as for interference and cell calculations, which are the basis for the high-level network planning process. In a GSM/DCS-system the high-level...

A machine learning algorithm for combining predictions is applied to seasonal predictions of the NINO3.4 index from six coupled atmosphere-ocean models. The algorithm adaptively tracks a dynamic sequence of “best experts” and produces a probability that a particular expert is best. Averaging based on this probability effectively yields a multi-model prediction. The algorithm gives seasonal pred...

Prediction is found to be a part of many more complex reasoning problems, e.g. state estimation , planning and diagnosis. In spite of this, the prediction problem is rarely studied on its own. Yet, there appears to be a wide range of choices for the design of the central component in a solution to this problem, the predictive model. We examine some of the alternatives and, as a case study, pres...

This report describes investigations performed for the prediction of the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction is derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave Desert. In addition to a model for power predictio...

inear prediction modelling is used in a diverse area of applications, such as data forecasting, speech coding, video coding, speech recognition, model-based spectral analysis, model-based interpolation, signal restoration, and impulse/step event detection. In the statistical literature, linear prediction models are often referred to as autoregressive (AR) processes. In this chapter, we introduc...

pollution control is based on correctly measuring pollutant concentrations but, at the moment,the principal european cities only monitor pollution levels by means of a few fixed stations co-located inselected points of the urban layout far from hotspots and/or by means of meteorological prediction models offuture pollution. few cities integrate the information from both sources. the implementat...