Isolating Stock Prices Variation with Neural Networks

In this study we aim to define a mapping function that relates the general index value among a set of shares to the prices of individual shares. In more general terms this is problem of defining the relationship between multivariate data distributions and a specific source of variation within these distributions where the source of variation in question represents a quantity of interest related to a particular problem domain. In this respect we aim to learn a complex mapping function that can be used for mapping different values of the quantity of interest to typical novel samples of the distribution. In our investigation we compare the performance of standard neural network based methods like Multilayer Perceptrons (MLPs) and Radial Basis Functions (RBFs) as well as Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM). As a reference benchmark of the prediction accuracy we consider a simple method based on the average values over certain intervals of the quantity of interest that we are trying to isolate (the so called Sample Average (SA) method). According to the results, MLPs and RBFs outperform MDNs and the GTM for this one-to-many mapping problem

Microgeographical, inter-individual, and intra-individual variation in the flower characters of Iberian pear Pyrus bourgaeana (Rosaceae)

Flower characteristics have been traditionally considered relatively constant within species. However, there are an increasing number of examples of variation in flower characteristics. In this study, we examined the variation in attracting and rewarding flower characters at several ecological levels in a metapopulation of Pyrus bourgaeana in the Doñana area (SW Spain). We answered the following questions: what are the variances of morphological and nectar characters of flowers? How important are intra-individual and inter-individual variance in flower characters? Are there microgeographical differences in flower characters? And if so, are they consistent between years? In 2008 and 2009, we sampled flowers of 72 trees from five localities. For six flower morphological and two nectar characteristics, we calculated coefficients of variation (CV). The partitioning of total variation among-localities, among-individuals, and within-individuals was estimated. To analyze differences among localities and their consistency between years, we conducted generalized linear mixed models. The CVs of nectar characters were always higher than those of morphological characters. As expected, inter-individual variation was the main source of variation of flower morphology, but nectar characters had significant variation at both intra- and inter-individual levels. For most floral traits, there were no differences among localities. Our study documents that variation is a scale-dependent phenomenon and that it is essential to consider intra- and inter-individual variance when investigating the causes and consequences of variation. It also shows that single year studies of floral characters should be viewed with caution