The multivariate time series models as the linear stochastic models were used to forecast monthly average of the Persian Gulf surface temperature (PGST). The time series data of temperature for the period 1854-2007 for six sea grid points were considered as the input file of multivariate time series. Vector autoregressive processes were employed to perform multivariate time series. The auto-correlation function plot of the residuals for the fitted model, based on the Akaike's information criterion, has indicated that the residuals were uncorrelated. The monthly values of the PGST from January 2008 to December 2009 as the test data have been forecasted using the fitted models. The results have indicated that the correlation coefficient between the observed and predicted values for each grid point was about 0.99. The root mean square prediction error for each grid point was less than 0.7 degrees Celsius.

In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.

In this study, the factors that influence the price transmission of chicken were analyzed using the Markov- Switching VAR and weekly data for the years 2008-2012. The results showed that the non- linear behavior of price transmission model inputs and the price of one- day chicks, soybeans and corn influence on the prices of chicken meat. In this regard, it is suggested that policy- makers with implement the policy of "market adjustment" and the control of "marketing agents behavior", cause the reduction of price volatility chicken and increase the consumer welfare.