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At the end of December 2019, the spread of a new infectious disease was reported in Wuhan, China, caused by a new coronavirus and officially named Covid-19 by the World Health Organization. As the number of victims of the virus exceeded 1,000, the World Health Organization chose the official name Covid-19 for the disease, which refers to "corona", "virus", "disease" and the year 2019.
 The forecasting about Covid-19 can help the government make better decisions. In this paper, an objective approach is used for forecasting Covid-19 based on the statistical methods. The most important goal in this paper is to forecast the prevalence of coronavirus for confirmed, dead and improved cases and to estimate the duration of the management of this virus using the exponential smoothing method. The exponential smoothing family model is used for short time-series data. This model is a kind of moving average model that modifies itself. In other words, exponential smoothing is one of the most widely used statistical methods for time series forecasting, and the idea is that recent observations will usually provide the best guidance for the future. Finally, according to the exponential smoothing, we will provide some suggestions.

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‎‎In this research‎, ‎the aim is to assess and analyze a method to predict the stock market‎. ‎However‎, ‎it is not easy to predict the capital market due to its high dependence on politics‎ ‎b‎ut by data modeling‎, ‎it will be somewhat possible to predict the stock market in the long period of time‎. ‎In this regard‎, ‎by using the semi-parametric regression models and support vector regression‎ ‎with different ‎kernels‎ and measuring the predictor errors in the stock market of one stock based on daily fluctuations and comparing methods using the root ‎of ‎mean ‎squared‎ error and mean absolute percentage error criteria‎, ‎support vector regression model ‎has ‎been‎ the most appropriate fit to the real stock market data with radial kernel and error equal to 0.1‎‎.