Search results for " Smooth"

Smoothed Particle Electromagnetics Modelling on HPC-GRID Environment

2012

In this paper a meshless approach on a high performance grid computing environment to run fast onerous electromagnetic numerical simulations, is presented. The grid computing and the message passing interface standard have been employed to improve the computational efficiency of the Smoothed Particle Electromagnetics meshless solver adopted. Applications involving an high number of particles can run on a grid computational environment simulating complex domains not accessible before and offer a promising approach for the coupling of particle models to continuous models. The used meshless solver is straightforward to program and fully parallelizable. The results of the parallel numerical sch…

An improved Smoothed Particle ElectroMagnetics method in 3D time domain simulations

2012

In this paper, an enhanced variant of the meshless smoothed particle electromagnetics (SPEM) method is performed in order to solve PDEs in time domain describing 3D transient electromagnetic phenomena. The method appears to be very efficient in approximating spatial derivatives in the numerical treatment of Maxwell's curl equations. In many cases, very often, accuracy degradation, due to a lack of particle consistency, severely limits the usefulness of this approach. A numerical corrective strategy, which allows to restore the SPEM consistency, without any modification of the smoothing kernel function and its derivatives, is presented. The method allows to restore the same order of consiste…

P-spline quantile regression: a new algorithm for smoothing parameter selection

SIOPRED performance in a Forecasting Blind Competition

2012

In this paper we present the results obtained by applying our automatic forecasting support system, named SIOPRED, over a data set of time series in a Forecasting Blind Competition. In order to apply our procedure for providing point forecasts it has been necessary to develop an interactive strategy for the choice of the suitable length of the seasonal cycle and the seasonality form for a generalized exponential smoothing method, which have been obtained using SIOPRED. For the choice of those essential characteristics of forecasting methods, also a certain multi-objective formulation which minimizes several measures of fitting is used. Once these specifications are established, the model pa…

Surface soil water content estimation based on thermal inertia and Bayesian smoothing

2014

Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, , the accuracy of the model may be questioned due to spatial and temporal varia…

First Experiences on an Accurate SPH Method on GPUs

2017

It is well known that the standard formulation of the Smoothed Particle Hydrodynamics is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. Moreover, the method is computational demanding when a high number of data sites and evaluation points are employed. In this paper an enhanced version of the method is proposed improving the accuracy and the efficiency by using a HPC environment. Our implementation exploits the processing power of GPUs for the basic computational kernel resolution. The performance gain demonstrates the method to be accurate and suitable to deal with large sets of data.

Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach

2017

Summary This paper is concerned with interval estimation for the breakpoint parameter in segmented regression. We present score-type confidence intervals derived from the score statistic itself and from the recently proposed gradient statistic. Due to lack of regularity conditions of the score, non-smoothness and non-monotonicity, naive application of the score-based statistics is unfeasible and we propose to exploit the smoothed score obtained via induced smoothing. We compare our proposals with the traditional methods based on the Wald and the likelihood ratio statistics via simulations and an analysis of a real dataset: results show that the smoothed score-like statistics perform in prac…

Forecasting time series with missing data using Holt's model

2009

This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.

Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data

2007

Abstract This paper provides a formulation for the additive Holt–Winters forecasting procedure that simplifies both obtaining maximum likelihood estimates of all unknowns, smoothing parameters and initial conditions, and the computation of point forecasts and reliable predictive intervals. The stochastic component of the model is introduced by means of additive, uncorrelated, homoscedastic and Normal errors, and then the joint distribution of the data vector, a multivariate Normal distribution, is obtained. In the case where a data transformation was used to improve the fit of the model, cumulative forecasts are obtained here using a Monte-Carlo approximation. This paper describes the metho…

Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data

2013

When collections of functional data are too large to be exhaustively observed, survey sampling techniques provide an effective way to estimate global quantities such as the population mean function. Assuming functional data are collected from a finite population according to a probabilistic sampling scheme, with the measurements being discrete in time and noisy, we propose to first smooth the sampled trajectories with local polynomials and then estimate the mean function with a Horvitz-Thompson estimator. Under mild conditions on the population size, observation times, regularity of the trajectories, sampling scheme, and smoothing bandwidth, we prove a Central Limit theorem in the space of …