Local global neural networks:: A new approach for nonlinear time series modeling

• 成果类型: Article
• 署名作者: Suárez-Fariñas, M; Pedreira, CE; Medeiros, MC
• 署名单位: Pontificia Universidade Catolica do Rio de Janeiro; Pontificia Universidade Catolica do Rio de Janeiro
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001691
• 发表日期: 2004
• 页码: 1092-1107
• 关键词: multilayer feedforward networks; selection approach; linear-models; regression; identification; specification; derivatives; mixtures; experts

摘要

We propose the local-global neural networks model within the context of time series models. This formulation encompasses some already existing nonlinear models and also admits the mixture of experts approach. We emphasize the linear expert case and extensively discuss the theoretical aspects of the model: stationarity conditions, existence, consistency and asymptotic normality of the parameter estimates, and model identifiability. The proposed model consists of a mixture of stationary and nonstationary linear models and is able to describe intermittent dynamics; the system spends a large fraction of time in a bounded region, but sporadically develops an instability that grows exponentially for some time and then suddenly collapses. Intermittency is a commonly observed behavior in ecology and epidemiology, fluid dynamics, and other natural systems. A model-building strategy is also considered, and the parameters are estimated by concentrated maximum likelihood. The procedure is illustrated with two real time series.