کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410148 679124 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Time-dependent series variance learning with recurrent mixture density networks
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Time-dependent series variance learning with recurrent mixture density networks
چکیده انگلیسی

This paper presents an improved nonlinear mixture density approach to modeling the time-dependent variance in time series. First, we elaborate a recurrent mixture density network for explicit modeling of the time conditional mixing coefficients, as well as the means and variances of its Gaussian mixture components. Second, we derive training equations with which all the network weights are inferred in the maximum likelihood framework. Crucially, we calculate temporal derivatives through time for dynamic estimation of the variance network parameters. Experimental results show that, when compared with a traditional linear heteroskedastic model, as well as with the nonlinear mixture density network trained with static derivatives, our dynamic recurrent network converges to more accurate results with better statistical characteristics and economic performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 122, 25 December 2013, Pages 501–512
نویسندگان
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