کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410756 679162 2008 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Long-term time series prediction with the NARX network: An empirical evaluation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Long-term time series prediction with the NARX network: An empirical evaluation
چکیده انگلیسی

The NARX network is a dynamical neural architecture commonly used for input–output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architecture of the NARX network can be easily and efficiently applied to long-term (multi-step-ahead) prediction of univariate time series. We evaluate the proposed approach using two real-world data sets, namely the well-known chaotic laser time series and a variable bit rate (VBR) video traffic time series. All the results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN and Elman architectures.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 71, Issues 16–18, October 2008, Pages 3335–3343
نویسندگان
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