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Showing 2 results for Jahadi
Dr Mahdi Roozbeh, Mr Arta Rouhi, Fatemeh Jahadi, Saeed Zalzadeh, Volume 26, Issue 2 (3-2022)
Abstract
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.
Mr Arta Roohi, Ms Fatemeh Jahadi, Dr Mahdi Roozbeh, Volume 27, Issue 1 (3-2023)
Abstract
The most popular technique for functional data analysis is the functional principal component approach, which is also an important tool for dimension reduction. Support vector regression is branch of machine learning and strong tool for data analysis. In this paper by using the method of functional principal component regression based on the second derivative penalty, ridge and lasso and support vector regression with four kernels (linear, polynomial, sigmoid and radial) in spectroscopic data, the dependent variable on the predictor variables was modeled. According to the obtained results, based on the proposed criteria for evaluating the goodness of fit, support vector regression with linear kernel and error equal to $0.2$ has had the most appropriate fit to the data set.
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