[1] With a validated Quasi‐Analytical Algorithm, an 11 year (1998–2008) monthly time series of the primary optical properties of waters in the center of the South Pacific gyre was developed from Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Also derived are chlorophyll a (Chl a) concentrations with the operational empirical algorithms...

BACKGROUND Childhood diarrhea continues to be a public health problem in developing countries, including Ethiopia. Detecting clusters and trends of childhood diarrhea is important to designing effective interventions. Therefore, this study aimed to investigate spatiotemporal clustering and seasonal variability of childhood diarrhea in northwest Ethiopia. METHODS Retrospective record review of...

Temperature is one of the most important climate variables that many economic and social plans related to its changes. Because of The importance of this issue, this study investigated the changes of it in the province Khuzestan. Meteorological data that used in this study is mean temperature in annual and seasonal timescale. The study area is Abadan, Ahwaz, Dezful and Omidiye stations. For anal...

3 We examine the effect of damping X-12-ARIMA's estimated seasonal variation on the accuracy of its seasonal adjustments of time series. Two methods for damping seasonals are proposed. In a simulation experiment, we generated time series data for each of 90 distinct experimental conditions that, in aggregate, characterize the variety of monthly series in the M3-competition. X-12-ARIMA consisten...

This paper studies two types of seasonal time series models with periodic variances. Covariance structures of the noise component in the models are discussed. For parameters in the regression component, the performance of the least squares estimates relative to the best linear unbiased estimates is investigated, and some lower bounds for the eecient coeecient deened by the covariance matrices o...

In this study, a hybrid approach based on seasonal decomposition (SD) and least squares support vector regression (LSSVR) model is proposed for air passenger forecasting. In the formulation of the proposed hybrid approach, the air passenger time series are first decomposed into three components: trend-cycle component, seasonal factor and irregular component. Then the LSSVR model is used to pred...

We have developed a conventional benchmark model for the prediction of two days electricity consumption industrial and institutional customers an provider. This task predicting 96 values 15 min per day in one shot is successfully dealt with by dynamic regression that uses Seasonal Trend decomposition method (STL) estimation trend seasonal components based on (approximately) three years real dat...

Seasonal variations in blood pressure have been consistently reported. However, uncertainty remains about the size of the seasonal effect in different regions, and about factors that explain the differences observed across and within populations. Using data from a national panel study, we investigated seasonal variations in blood pressure in the South African adult population, and whether these...

A statistical framework is presented for the assessment of climatological trends in the frequency of rare and extreme weather events. The methodology applies to long-term records of event counts and is based on the stochastic concept of binomial distributed counts. It embraces logistic regression for trend estimation and testing, and includes a quantification of the potential/limitation to disc...

PURPOSE To evaluate the effects of bimatoprost 0.03% single treatment for 3 years on intraocular pressure (IOP) and visual field performance. METHODS We monitored the IOP of 62 patients with normal-tension glaucoma every 1-3 months. The Humphrey visual field test was performed every 6 months after treatment and the results obtained were compared to those before treatment. In addition, visual ...