Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series

In this paper, an analysis of kernel (GP) and feed-forward neural networks (FFNN) based filter to forecast short rainfall time series is presented. For the FFNN, the learning rule used to adjust the filter weights is based on the Levenberg-Marquardt method and Bayesian approach by the assumption of the prior distributions. In addition, a heuristic law is used to relate the time series roughness with the tuning process. The input patterns for both NN-based and kernel models are the values of rainfall time series after applying a time-delay operator. Hence, the NN´s outputs will tend to approximate the current value of the time series. The time lagged inputs of the GP and their covariance functions are both determined via a multicriteria genetic algorithm, called NSGA-II. The optimization criteria are the quantity of inputs and the filter´s performance on the known data which leads to Pareto optimal solutions. Both filters -FFNN and GP Kernel- are tested over a rainfall time series obtained from La Sevillana establishment. This work proposed a comparison of well-known filter referenced in early work where the contribution resides in the analysis of the best horizon of the forecasted rainfall time series proposed by Bayesian adjustment. The performance attained is shown by the forecast of the next 15 months values of rainfall time series from La Sevillana establishment located in (-31° 1´22.46"S, 62°40´9.57"O) Balnearia, Cordoba, Argentina.http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6706741&isnumber=6706705Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina.Fil: Pucheta, J. Universidad Nacional de Córdoba; Argentina.Fil: Patiño, H. Universidad Nacional de Córdoba; Argentina.Fil: Baumgartner, J. Universidad Nacional de Córdoba; Argentina.Fil: Laboret, S. Universidad Nacional de Córdoba; Argentina.Fil: Sauchelli, V. Universidad Nacional de Córdoba; Argentina.Sistemas de Automatización y Contro

Filtro predictor basado en redes neuronales para pronóstico de series temporales de lluvia acumulada empleando submuestreo

En éste trabajo se presenta un filtro predictor basado en redes neuronales (RNs) directas para pronóstico de series temporales de alta rugosidad empleando submuestreo, contribuyendo a la nueva generación de herramientas que permiten conocer la previsibilidad de agua de lluvia. Se generan series temporales a partir de submuestrear a una serie dato original, partiendo del valor disponible más reciente hacia el más antiguo. Se usaron series provenientes de la Ecuación Mackey-Glass (MG) de 120 datos, donde se usaron para validar al algoritmo los últimos 18 valores. También se usó una serie de lluvia mensual acumulada proveniente del establecimiento Santa Francisca, Alta Gracia, Córdoba, que tiene 125 valores. Para cada una de las series generadas por el submuestreo, se ajustó a un filtro diferente basado en RNs, y cada uno de ellos genera un pronóstico que luego es promediado en su conjunto. La regla de ajuste utilizada en el proceso de aprendizaje se basa en el método Levenberg-Marquard y el desempeño del filtro propuesto se evalúa a través del índice SMAPE. En muchos casos se obtienen mejoras muy notorias respecto del resultado obtenido mediante el filtro basado en RNs sin submuestreo.In this work, a neural networks (NN) -based predictor filter for forecasting cumulative rainfall sub-sampled time series of high roughness is presented. It is intended to contribute to the generation of tools to ascertain the predictability of rainfall. Time series are generated from a series down sample the original data, based on the latest available value to the oldest. Using series from the Mackey-Glass Equation (MG) 120 data, which were used the last 18 values to validate the algorithm . Series of monthly rainfall accumulated from Santa Francisca, Alta Gracia, Cordoba, were used which consist of 125 values. For each series generated by sub- sampling, was adjusted to a different filter based on NN, and each one generates a forecast that is then averaged together. The adjustment rule used in the learning process is based on the Levenberg-Marquard method and the proposed filter performance is evaluated by SMAPE index. In many cases very noticeable improvements are obtained with respect to the result obtained by filter based on NN without down sampling.Sociedad Argentina de Informática e Investigación Operativ

Energy associated tuning method for short-term series forecasting by complete and incomplete datasets

This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations

Intracellular signaling cascades triggered by the NK1 fragment of hepatocyte growth factor in human prostate epithelial cell line PNT1A

Hepatocyte Growth Factor (HGF)/c-MET signaling has an emerging role in promoting cell proliferation, survival, migration, wound repair and branching in a variety of cell types. HGF plays a crucial role as a mediator of stromal–epithelial interactions in the normal prostate but the precise biological function of HGF/ c-Met interaction in the normal prostate and in prostate cancer is not clear. HGF has two naturally occurring splice variants and NK1, the smallest of these HGF variants, consists of the HGF amino terminus through the first kringle domain. We evaluated the intracellular signaling cascades and the morphological changes triggered by NK1 in human prostate epithelial cell line PNT1A which shows molecular and biochemical properties close to the normal prostate epithelium. We demonstrated that these cells express a functional c- MET, and cell exposure to NK1 induces the phosphorylation of tyrosines 1313/1349/1356 residues of c-MET which provide docking sites for signaling molecules. We observed an increased phosphorylation of ERK1/2, Akt, c-Src, p125FAK, SMAD2/3, and STAT3, down-regulation of the expression of epithelial cell–cell adhesion marker E-cadherin, and enhanced expression levels of mesenchymal markers vimentin, fibronectin, vinculin, α-actinin, and α-smooth muscle actin. This results in cell proliferation, in the appearance of a mesenchymal phenotype, in morphological changes resembling cell scattering and in wound healing. Our findings highlight the function of NK1 in non-tumorigenic human prostatic epithelial cells and provide a picture of the signaling pathways triggered by NK1 in a unique cell line