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.  

In this paper, we propose a new residual Analysis method using Fourier Series transform into fuzzy Time Series model for improving the forecast- ing performance. This hybrid model takes advantage of the high predictable power of fuzzy Time Series model and Fourier Series transform to t the esti- mated residuals into frequency spectra, select the low-frequency terms, lter out high-frequency terms, and then have well forecasting performance. Two numerical examples are given to show that our proposed model can be applied with the best forecasting performance than the other models.

Nowadays, the maximum operation of groundwater resources has been achieved in Iran. Also, the majority of extractable water resources are utilized and the managing of water resources in the future is depended on more extracting of water resources. For better basin management, forecasting the groundwater depth fluctuations in particular in arid areas is more necessary. In this study, Time Series spectral Analysis is used to forecast the groundwater depth fluctuations of Chamchamal plain. In this regard, the monthly groundwater depth Time Series during 1995 to 2009 years are used for calibration periods and the periodogram diagrams are depicted. Data periodicity is analyzed by using Fourier spectral Analysis and the deterministic term of data periodicity is eliminated. In the next step, stationary and normality in the data are considered. After that, the different Time Series models are fitted for the prepared data and accuracy of them were assessed by Akaike (AIC) criterion. The results show that ARMA (2, 1), ARMA (1, 1), ARMA (1, 1) models are the best fitted models for the measured data in Bazanabad, Gheshlaghabad and Gavkol piezometers, respectively. Finally, the residuals stationarity assumption test is used to check for the correct diagnosis of the fitted pattern. In this study, the results represent the high performance and accuracy of the applied new approach to the Time Series spectral Analysis for forecasting groundwater depth by application of the regression coefficient amount of 0. 78 and SI-Index of 4% to 14% of piezometers' data. Using spectral Analysis, as has been provided in this study, is very useful for forecasting groundwater depth.

every Series of Wavelet coefficients includes part of Time Series in the scale of different Time Series. Implementation of the wavelet transform, using the best Wavelet at the right levels has significant impact on the results of the results of the financial Analysis.the purpose of this study is to explanation of the importance of the concept of scale-Time and the use of different Time intervals in checking the behavior of the financial markets to be determined whether the removing noise from the Time Series can accurate the decisions we have to make in the future or not.Therefore we analyzed 16 selected index of the Tehran Stock Exchange using software "R" and using Wavelet transformation up to five levels for 250 data then put them all under noise removing process. In the next step we used two methods for evaluation the noise removing process.one clustering all the selected index in the dendrogram method And the other one Time Series predictions of total index which includes 500 data and the use of the data that has been noise removed into two methods of Haar wavelet and Daubechies.The results of both method claim better performance using Wavelet removing noise using Daubechies wavelet in this Series. our main goal is using the wavelet Analysis and noise removing from Time Series and using that in financial topics.