دانلود مقاله ISI انگلیسی شماره 52132
ترجمه فارسی عنوان مقاله

روش هوش محاسباتی و مدل های خطی در مورد پیش بینی نرخ ارز

عنوان انگلیسی
Computational intelligence approaches and linear models in case studies of forecasting exchange rates
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
52132 2007 8 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 33, Issue 4, November 2007, Pages 816–823

ترجمه کلمات کلیدی
شبکه های عصبی؛ سیستم های فازی؛ پیش بینی؛ مدل های خطی؛ نرخ تبدیل
کلمات کلیدی انگلیسی
Neural networks; Fuzzy systems; Forecasting; Linear models; Exchange rates
پیش نمایش مقاله
پیش نمایش مقاله  روش هوش محاسباتی و مدل های خطی در مورد پیش بینی نرخ ارز

چکیده انگلیسی

Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional auto regressive moving average (ARMA) and ARMA generalized auto regressive conditional heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min, 60 min and 120 min, daily and weekly basis, the one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series’ frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models.