Control charts are the most useful tools for controlling the processes statistically. The construction of the control charts requires the estimation of the process parameters using random sample DATA. Usually the classical estimators of the process parameters are used to construct the control charts. The classical estimators of the parameters of the processes generating auto correlated DATA are sensitive to the presence of the outlier observations. Applying classical methods of estimation while outliers are present, introduce biased estimates of the model parameters which result in wrong interpretation of the control chart. In this research a method called Iteratively Robust Filtered Fast Tau (IRFFT) which is insensitive to the presence of the outliers is proposed for estimating the parameters of the auto correlated models. The newly introduced estimators are used to construct robust control chart for auto correlated DATA. The suggested robust control chart is compared with the control chart whose parameters are estimated using LS method. Results of the simulation study for the two methods indicate that the ARL for the suggested robust control chart is much smaller under different scenarios. The findings may be extended to the other TIME SERIES models.

Regression diagnostic is the general class of technique for detecting outliers and influential points. Our objective is to extend these diagnostics to the TIME SERIES setting. In particular, various diagnostic tools will be explored for autoregressive model, which resembles the multiple regression models.In this research first the concept of leverage and Cook’sdistance based on the idea of removing one observation at a TIME and measuring the change in the fitted model, will be adapted to the TIME SERIES situation. Then, since in some TIME SERIES the outliers occur in patches, therefore it seems that it is better to consider some sort of diagnostics, such as multiple deletion statistic and multiple deletion Cook’s distance, based on the idea of leaving out a block of k consecutive observations.To detect outliers each of these procedures needs a threshold. Values of these statistics that exceed the threshold could be suspected as being an outlier. Since the distributional properties of these statistics are not known precisely, therefore to overcome this problem some simulation study were carried out. Finally, these diagnostic approaches will be used for some real TIME SERIES DATA.

Accurate forecasting of streamflows has been one of the most important issues playing a key role in allotment of water resources. River flow simulations to determine the future river flows are important and practical. Given the importance of flow in the coming years, in this research three stations were simulated in 2002-2011: Haji Qooshan, Ghare Shoor and Tamar in Gorganrood Cachment. To simulate river flow, TIME SERIES (Auto Regression) and DATA driven based on support vector machine (SVM) was used for both monthly and weekly. The results showed that both methods in Tamar have low precision and Haji Qooshan station have good precision in monthly simulation. SVM increase 0.29 coefficient determination and decreases 0.35 RMSE error in Ghare Shoor station and perform more accurate than TIME SERIES. Both methods simulate weekly discharge in low precision in Tamar and Ghare Shoor. Coefficient determination of TIME SERIES is 0.91 and SVM is 0.86 in weekly simulation. DDR statistics show that the SVM has greater precision than TIME SERIES in monthly simulation and equal precision in weekly simulation in Haji Qooshan station. The results of this study show that the SVM method is more accurate than TIME SERIES in monthly and weekly simulation. The accuracy of both methods is on monthly basis rather than weekly. The accuracy of both methods is greater on monthly rather than weekly.

In this paper annual and small duration TIME SERIES of Caspian Sea level fluctuations were modeled using Topex/Poseidon and Jason- 1 alTIMEtry DATA by statistical methods from September 1992 to September 2007” To take into consideration crossover points of ground tracks, Caspian Sea covered with seven crossover passes. By means of principal component analysis technique, seven TIME SERIES converted into one SERIES. The Caspian sea level changes showed rising at the rate of +13.7 cm/yr between January 1993 and June 1995. However, by June 1995, the sea level started to drop abruptly, a trend was still observed in January 2001. From middle 1995 to March 1996 the rate of the Caspian Sea level drop was −34.0 cm/yr, and later it decreased a rate −29.3 cm/yr (June 1996- April 1997). From July 1998 to January 2001 rate of sea level decrease was −5.1 cm/yr. From January 2001 to March 2007 the sea level was rising at the rate of +5.5 cm/yr. With comparison of alTIMEtry and level gages DATA in several stations we could observe high correlations. Correlation between alTIMEtry and level gages DATA in Anzali, oil stokes station and mean of three stations in north part of Caspian Sea (Machachkala, Krasnovodsk and fort- Shevchenko) was 95.6, 97.3 and 77.3 percent respectively.

There are many studies discussing the correlation between air pollution and human health hazards. Yet, in Tehran there is not a survey using TIME SERIES methodology. Thus, we conducted a study based on TIME SERIES DATA on the topic in Tehran, Iran. Mean levels of NO, NO2, NOX, CO, 03, SO2 and PM10 (particulate matters smaller than 10micrometer in diameter) were measured in one station of Tehran's Air Quality Control Corporation and were used as main independent variables. Mean temperature, mean humidity, day of the week, month and season were considered as potential confounders and deaths in people older than 64 years in Tehran was the dependent variable. All the variables were measured during Mar. 1998 to Dec. 1999. Concentrations of air pollutants were different between seasons and so were the means of daily deaths. Out of main independent variables, SO2, CO and PM10 showed statistically significant relations with the dependent variable (P<0.05). After controling for confounders, there was 3.4%, 2.6% and 3.36% increase in death rates, respectively, for each interquartile ascending (increase from 25th centile to 75th centile) in association to the mentioned pollutant centile concentration. No autocorrelation between residuals was observed (r= -0.059). The study showed that meteorological variables can confound the relation between air pollution and rate of deaths per day.  

Parametric TIME SERIES models typically consists of model identification, parameter estimation, model diagnostic checking, and forecasting. However compared with parametric methods, nonparametric TIME SERIES models often provide a very flexible approach to bring out the features of the observed TIME SERIES. This paper suggested a novel fuzzy nonparametric method in TIME SERIES models with fuzzy observations. For this purpose, a fuzzy forward fit kernel-based smoothing method was introduced to estimate fuzzy smooth functions corresponding to each observation. A simple optimization algorithm was also suggested to evaluate optimal bandwidths and autoregressive order. Several common goodness-of-fit criteria were also extended to compare the performance of the proposed fuzzy TIME SERIES method compared to other fuzzy TIME SERIES model based on fuzzy DATA. Furthermore, the effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results indicate that the proposed model performs better than the previous ones in terms of both scatter plot criteria and goodness-of-fit evaluations.

In recent decades the world economy has witnessed vast changes that mainly occurred because of the emergence of factors that make up globalization. The purpose of this study is to evaluate the impact of financial globalization on stock return. In this respect, total foreign direct investment and investments in portfolios are employed as indicator of financial globalization. Also, among the other macroeconomic variables affecting the stock return, GDP, liquidity, inflation, market exchange rate and oil revenues are considered. This study applies the Bounds test and Autoregressive Distributed Lag (ARDL) models for Iranian economic during 1997-2015. The results provide a positive relationship between financial globalization and stock return. In other words, expansion of financial globalization lead to increase in stock return in Iran.

Today, the use of DATA mining techniques such as classification, clustering, discover repetitive pattern and discover outliers in different domains including production, medicine, social, meteorology, stock exchange, sales, customer service and other areas are increasing. DATA mining techniques are specifically designed for static DATA. Therefore, their use for TIME SERIES DATA requires some modifications to their respective algorithms. One of these changes is the selection of the appropriate similarity measurement method, because similarity measurement methods are used in all DATA mining techniques. Therefore, in this research, we will evaluate and compare the effect of two commonly used and efficient methods of TIME SERIES similarity measurement in DATA mining. This evaluation is done in relation to the effectiveness of these methods in achieving better results. These methods are the Longest Common Sub Sequence (LCSS) method and the Dynamic TIME Warping (DTW) method. The main purpose of this research is to compare the performance of these methods in TIME SERIES DATA mining. The DATA mining techniques that used in this research are the nearest-neighbor technique and k-medoids clustering algorithm. The performance evaluation process is described in the text. This process uses the nearest-neighbor technique to calculate the accuracy of detection of right TIME SERIES class, and uses the k-medoids clustering technique to calculate the clustering accuracy, the ability to correctly determine the number of clusters, and the ability to determine the better cluster representative. For this purpose, we use 63 TIME SERIES DATA sets by random from a world-renowned DATAbase that named UCR collection. The results show that the effect of LCSS method is significantly better than the effect of DTW method on the correct detection accuracy of TIME SERIES class and clustering accuracy by 99% and 92. 5% confidence, respectively, but there is no significant difference between them in terms of their effect in determining the number of clusters and cluster representatives. The results of this research help to use these methods in appropriate DATA mining techniques in issues such as customer segmentation, workshop scheduling and the like more accurately.