کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406236 678075 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks
ترجمه فارسی عنوان
پیش بینی شاخص های بازار سهام با استفاده از تجزیه و تحلیل جزء اصلی و زمان تصادفی شبکه های عصبی موثر
کلمات کلیدی
پیش بینی، شبکه عصبی مؤثر بر زمان استوکاست، تجزیه و تحلیل مولفه اصلی، مدل سری زمانی مالی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Financial market dynamics forecasting has long been a focus of economic research. A stochastic time effective function neural network (STNN) with principal component analysis (PCA) developed for financial time series prediction is presented in the present work. In the training modeling, we first use the approach of PCA to extract the principal components from the input data, then integrate the STNN model to perform the financial price series prediction. By taking the proposed model compared with the traditional backpropagation neural network (BPNN), PCA-BPNN and STNN, the empirical analysis shows that the forecasting results of the proposed neural network display a better performance in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, HS300, S&P500 and DJIA in the established model, and the corresponding statistical comparisons of the above market indices are also exhibited.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 156, 25 May 2015, Pages 68–78
نویسندگان
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