人工神经网络
自回归模型
人工智能
算法
系列(地层学)
蒙特卡罗方法
机器学习
计算机科学
非线性系统
数学
统计
数据挖掘
量子力学
生物
物理
古生物学
作者
Aastha M. Sathe,Neelesh S. Upadhye,Agnieszka Wyłomańska
标识
DOI:10.1016/j.cam.2022.115051
摘要
Recent research activities in forecasting suggest that artificial neural networks can be a promising alternative to the traditional linear models. However, no single model, either linear or nonlinear is capable of obtaining the forecasts accurately. In this paper, a hybrid methodology that combines symmetric α-stable autoregressive time series and artificial neural networks is proposed. The methodology is validated through Monte-Carlo simulations. Moreover, the new method is used to model real empirical data thus showing the usefulness of heavy-tailed models supported by artificial neural networks in statistical modeling.
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