In this study, we estimated the parameters using the State space model described in ARIMA form. We’ve also used the Monte Carlo Method for simulating the process in 10000 reputations. Then the estimated parameters and the Monte Carlo simulation method are used to forecast TEPIX index, including 739 observations as an in-sample data from 21th of January 2011 to 19th February 2014 and 59 observations from 20th February 2014 to 21th May 2014 as an out of sample data. Furthermore, For more investigation we’ve considered different horizons of forecasting, short-term (equal to 1 week), mid-term (equal to 1 month) and long term (equal to 3 month). The results showed that Tehran stock market data has enough efficiency to forecast them, and showed that the State space in Form ARIMA model and the Monte Carlo simulation method can be used as a predictive algorithm for TEPIX index and other indices with similar nature.

Regarding The Importance Of Forecasting And Its Precision And Accuracy Significance In Various Economic Conditions, This Research Is An Attempt For Denoising Of Stock, By Implementing Of Wavelet Transform (A Branch Of Physic And Specially Signal Analysis) Considering TEPIX Index. Results Indicate That Existence Of Noises Lead To Reduction In Predictive Power Of The Models And Denoising Cause To Improvement In Compatibility Of Dataset With The Models And Finally Improve Predictive Power Of The Models. In This Respect, The ARCH And State space Models And In-Sample Of 739 Daily Observations, Using The TEPIX Index Data Between Years 1389 – 1392, Are Implemented. The Results Clearly Indicate The Impactful Role Of Haar Wavelet Transform For Denoising TEPIX Dataset. This Method, Strongly, Lead To Increase In The Predictive Power Of The Models And Accuracy Of Estimated Coefficients.

The main objective of this paper is to compare the accuracy of ARIMA, GARCH, ARIMA-GARCH and State space models in estimating and forecasting the Tehran Exchange Stock Index (TEPIX). For this purpose, daily data from 21st of January 2011 to 19th February 2014 (corresponding to Iranian calendar: from first of Bahman 1389 to 30th of Bahman 1392) as out -of- sample and daily data from 20th of February 2014 to 19th May 2015 (corresponding to Iranian calendar: from first of Esfand 1392 to 30th of Ordibehesht 1393) as in- sample have been used. On the other hand, for more consideration and for boosting the accuracy of the mentioned models’ forecasting the TEPIX in the long run, simulation has been done using the Monte Carlo Method for two periods of time. These include the medium run and the short run, using out- of- sample and using in- sample for a general comparison. Next, the accuracy of the forecasts have been evaluated by calculating the Root Mean Square Errors (RMSE). According to the out- of- sample, the results indicate that the GARCH model has more accuracy of forecasting rather than the other models in the three periods of time (long run, medium run and short run), and compared with in-sample, the ARIMA model is a more sufficient model.

Factors such as improper performance of gates, their failure, and inappropriate design of gate dimensions are likely to be followed by dangerous dam overtopping. In this article, the nonlinear equations expressing the flow rate through the dam bottom gate or intake as well as the water head inside the reservoir were converted into linear equations in the State space. Based on these linear equations, the inlet and outlet hydrographs of the dam were plotted and the equilibrium point of the diagrams duly determined. Upon adjusting the equilibrium point between the inlet and outlet hydrographs and the height (head) and volume of the reservoir water, the bottom gate or intake dimensions were calculated. The inflow and outflow flood hydrographs fully overlap in case the smart prediction and flood control system along with the pulse method is used for routing of the flood to the reservoir, such that the difference between the two is negligible. Therefore, the proposed smart system offers sufficient accuracy. Then, the digital controllers and other electronic devices were built using microcontrollers, sensors, ultrasonic distance meters, and radio wave transmitters and receivers. Different programming languages were employed in the design building of systems. The proposed flood prediction and control system is equipped with alarm systems to inform the operators in the case of emergencies. After their design and building, the systems were repeatedly used and tested in the laboratory and open channels. The results were favourable and of high accuracy. This is a simple, useful, and reliable method, and can be a suitable substitute for previous ones.

Feyzi-Riazi equation of State [1] (FR-EOS) is modified in this paper. FR equation of State which is a modification of Peng-Robinson equation of State [2] (PR-EOS) predicts the volume of both liquid and vapor phases of nonpolar compounds accurately. But it fails in the prediction of the behavior of polar compounds.In addition to the reduced dipole moment the proposed equation requires critical temperature, critical pressure and acentric factor as the input parameters. The proposed equation of State estimates the specific volume of polar liquids and hydrogen-bonded fluids with greater accuracy while maintaining the ability of the FR-EOS in predicting vapor pressure and critical compressibility factor of pure compounds and mixtures.

This paper is investigating the mixture autoregressive model with constant mixing weights in State space form and generalization to ARMA mixture model. Using a sequential Monte Carlo method, the forecasting, filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM algorithm. The results show the dimension of parameter vector in State space representation reduces. The results of the simulation study show that the proposed filtering algorithm has a steady State close to the real values of the State vector. Moreover, according to simulation results, the mean vectors of filtering and smoothing distribution converges to State vector quickly.