Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

A time series sequestration and storage model of atmospheric carbon dioxide

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Time series analysis for minority game simulations of financial markets

The minority game (MG) model introduced recently provides promising insights into the understanding of the evolution of prices, indices and rates in the financial markets. In this paper we perform a time series analysis of the model employing tools from statistics, dynamical systems theory and stochastic processes. Using benchmark systems and a financial index for comparison, several conclusions are obtained about the generating mechanism for this kind of evolution. The motion is deterministic, driven by occasional random external perturbation. When the interval between two successive perturbations is sufficiently large, one can find low dimensional chaos in this regime. However, the full motion of the MG model is found to be similar to that of the first differences of the SP500 index: stochastic, nonlinear and (unit root) stationary. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Integrable NLS equation with time-dependent nonlinear coefficient and self-similar attractive BEC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Formation of multiple or adventive embryos in Epidendrum nocturnum Jacq (Orchidaceae)

Under greenhouse conditions, Epidendrum nocturnum Jacq. plants produce fruits by both self-fertilization and cleistogamy. Although adapted to these reproductive processes the species respond also to cross-pollination. Seeds without embryos and with one embryo are usual but occasionally seeds with two, three or four embryos are produced. Multiple embryos are formed by polyembryony and apomixis. © 1985 Annals of Botany Company.

Neither plasma progesterone concentrations nor exogenous eCG affects rates of ovulation or pregnancy in fixed-time artificial insemination (FTAI) protocols for puberal Nellore heifers

The objective was to evaluate the effects of plasma progesterone (P4) concentrations and exogenous eCG on ovulation and pregnancy rates of pubertal Nellore heifers in fixed-time artificial insemination (FTAI) protocols. In Experiment 1 (Exp. 1), on Day 0 (7 d after ovulation), heifers (n = 15) were given 2 mg of estradiol benzoate (EB) im and randomly allocated to receive: an intravaginal progesterone-releasing device containing 0.558 g of P4 (group 0.5G, n = 4); an intravaginal device containing 1 g of P4 (group 1G, n = 4); 0.558 g of P4 and PGF2α (PGF; 150 μg d-cloprostenol, group 0.5G/PGF, n = 4); or 1 g of P4 and PGF (group 1G/PGF, n = 3). On Day 8, PGF was given to all heifers and intravaginal devices removed; 24 h later (Day 9), all heifers were given 1 mg EB im. In Exp. 2, pubertal Nellore heifers (n = 292) were treated as in Exp. 1, with FTAI on Day 10 (30 to 36 h after EB). In Exp. 3, pubertal heifers (n = 459) received the treatments described for groups 0.5G/PGF and 1G/PGF and were also given 300 IU of eCG im (groups 0.5G/PGF/eCG and 1G/PGF/eCG) at device removal (Day 8). In Exp. 1, plasma P4 concentrations were significantly higher in heifers that received 1.0 vs 0.588 g P4, and were significantly lower in heifers that received PGF on Day 0. In Exp. 2 and 3, there were no significant differences among groups in rates of ovulation (65-77%) or pregnancy (Exp. 2: 26-33%; Exp. 3: 39-43%). In Exp. 3, diameter of the dominant ovarian follicle on Day 9 was larger in heifers given 0.558 g vs 1.0 g P4 (10.3 ± 0.2 vs 9.3 ± 0.2 mm; P < 0.01). In conclusion, lesser amounts of P4 in the intravaginal device or PGF on Day 0 decreased plasma P4 from Days 1 to 8 and increased diameter of the dominant follicle on Day 9. However, neither of these nor 300 IU of eCG on Day 8 significantly increased rates of ovulation or pregnancy. © 2011.