Prediction on complex time series has received much attention during the last decade. This paper reviews least square and radial basis function based predictors and proposes a support vector regression (SVR) based local predictor to improve phase space prediction of chaotic time series by combining the strength of SVR and the reconstruction properties of chaotic dynamics. The proposed method is...

Reverse phase protein arrays (RPPA) measure the relative expression levels of a protein in many samples simultaneously. Observed signal from these arrays is a combination of true signal, additive background, and multiplicative spatial effects. Background subtraction alone is not sufficient to remove all nonbiological trends from the data. We developed a surface adjustment that uses information ...

this study evaluated the effects of various dietary levels of maros nigra's powder on some immune factors and blood parameters of rainbow trout (oncorhynchus my kiss). 320 fish with average initial weight 75 + 0.01 g randomly distributed in 12 tanks and fed by 0, 20, 40, and 60 ppm additive powder for 8 weeks. the results showed that the treatments, which fed more additive powder, had the ...

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used ge...

To some extend, many complicated nonlinear maps are additive models of a number of linear and nonlinear terms. A single linear model or nonlinear model (i.e., a neural network model) has its limitation for approximating this class of maps. In this paper, a hybrid approach to evolve an additive tree model for a given problem is proposed. In this approach, tree-structure based evolution algorithm...

Quality Protein Maize (QPM) has about twice the amount of lysine and tryptophan of normal maize and hence represents an important tool of correcting its deficiency in protein quality. However, the effects of low nitrogen and drought on genetic parameters such as gene action and combining abilities of protein quantity and quality of QPM are not known. To study how these genetic parameters are af...

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for...

introduction weeds are main factors reducing crops yield, especially under organic farming conditions (). it has been reported that weed populations are more in organic farming compared to conventional cropping systems, resulting in more reduction of growth and yield. although the chemical control is a fast and effective way for controlling weed populations, some negative impacts of the recent ...

ABSTRACT There has been growing interest in exploiting potential forecast gains from the nonlinear structure of self-exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR-type nonlinearities in observed time series. However, previous studies show that classical nonlinearity tests are not robust t...