Abstract In the present study, seasonal autoregressive integrated moving average (SARIMA) time series models, nonlinear BL model, multi-layer perceptron artificial neural network and SARIMA-bilinear hybrid models were employed to predict quality parameters of total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC) in Maroon basin Khuzestan Province. For fitting...

Many variations such as the annual cycle of sea surface temperatures can be considered to be smooth functions and are appropriately described using methods from functional data analysis. This study de-nes a class of Functional Auto-Regressive (FAR) models which can be used as robust predictors for making forecasts of entire smooth functions in the future. The methods are illustrated and compare...

As a supplement to or extension of methods used to determine trends in foodborne illness over time, we propose the use of Internet search metrics. We compared Internet query data for foodborne illness syndrome-related search terms from the most popular 5 Korean search engines using Health Insurance Review and Assessment Service inpatient stay data for 26 International Classification of Diseases...

BACKGROUND Determining the temporal variations and seasonal pattern of diseases and forecasting their incidence can help in promoting disease control and management programs. This study was performed to determine the seasonal variation of tuberculosis and forecast its incidence until the year 2015 in one of the northern provinces of Iran. MATERIALS AND METHODS A longitudinal time series study...

To assess the groundwater potential, analyses of trend decomposition, time series and frequency between various types of data in hydrologic setting and between different basins is required for local and regional studies. In this paper, ground water level fluctuations and Nitrate concentrations of kabudarahang aquifer were investigated with application of time series models. Simulated values wer...

The objective of this study was to model seasonal behavior of broiler price in Iran that can be used to forecast the monthly broiler prices. In this context, the periodic autoregressive (PAR), the seasonal integrated models, and the Box-Jenkins (SARIMA) models were used as the primary nominates for the forecasting model. It was shown that the PAR (q) model could not be considered as an appropri...

prediction of hydrological variables is a highly effective tool in water resource management. One of the important tools for modeling hydrological processes is the use of time series modeling and analysis. River series production series can be used by time series models in various studies such as drought, flood, reservoir systems design and many other purposes For this purpose, monthly flow dat...

Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data ava...

Abstract Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates benefits and downfalls univariate streamflow modeling at high latitudes using GP seasonal autoregressive integrated moving average (SA...