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	Non-parametric time series models for hydrological forecasting
	Xia J.
	2007
关键词	averaged method backfitting technique forecasting functional-coefficient autoregression model transfer function model local polynomial method non-parametric and functional-coefficient autoregression model periodic autoregressive model Semi-parametric regression model neural-networks runoff
英文摘要	To perform hydrological forecasting, time series methods are often employed. In univariate time series, the autoregressive integrated moving average (ARIMA) model, the seasonal autoregressive moving average (SARMA) model, the deseasonalized model and the periodic autoregressive (PAR) model. are often used. These models are based on the assumption that the influence of tagged riverflows on the riverflow is linear. In reality the assumption is often questionable. In this paper, the functional-coefficient autoregression (FCAR) model, which is a nonlinear model, is introduced to forecast riverflows. To explore the influence of the inflow on the outflow in a river system and to exploit the internal interaction of the outflows, bivariate time series models are needed. The transfer function (TF) model and the semi-parametric regression (SPR) model are often employed. In this paper, a new model, the non-parametric and functional-coefficient autoregression (NFCAR) model, is proposed. It consists of two parts: the first part, the non-parametric part explains the influences of the inflows on the outflow in a river system; the second part, the functional-coefficient linear part reveals the interactions among the outflows in a river system. By comparing the calibration and forecasting of the models, it is found that the NFCAR model performs very well. (c) 2006 Elsevier B.V. All rights reserved.
出处	Journal of Hydrology
卷	332
期	3-4
页	337-347
收录类别	SCI
语种	英语
ISSN号	0022-1694
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/22802]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Xia J.. Non-parametric time series models for hydrological forecasting. 2007.

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