Geophysical model discrimination using the Akaike information criterion
Geophysical model discrimination using the Akaike information criterion
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DOI：
10.1109/tac.1981.1102597
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发表时间：
1981-04
影响因子：
6.8
通讯作者：
K. Hipel
中科院分区：
文献类型：
--
作者：
K. Hipel
开发了一种通用的建模程序，用于采用赤池信息准则（AIC）来选择最合适的随机模型，以描述特定的地球物理时间序列。为了证明所提出的建模方法的有效性，给出了许多类型随机模型的AIC公式，并且该方法成功地用于确定最合适的模型，以拟合水文学和自然科学中出现的各种各样的数据集。在对年度水文时间序列进行建模时，AIC用于区分短记忆模型和长记忆模型。设计了一种改进的约束自回归模型来描述每年的太阳黑子数，同时考虑了平稳和非平稳线性随机模型来对树木年轮序列进行建模。考虑了三种类型的模型来对季节性时间序列进行建模，并在一个实际应用中展示了如何选择最合适的模型来拟合每月的河流流量时间序列。该建模程序还用于选择一个干预模型，该模型描述了阿斯旺大坝对尼罗河年平均流量的影响。
A general model building procedure is developed for employing the Akaike information criterion (AIC) to select the most appropriate stochastic model to describe a specified geophysical time series. To demonstrate the effectiveness of the proposed approach to model construction, formulas for the AIC are given for many types of stochastic models and the method is successfully employed for determining the most suitable models to fit a wide variety of data sets which arise in hydrology and the natural sciences. The AIC is used to discriminate between short and long memory models when modeling annual hydrological time series. An improved constrained autoregressive model is designed for describing yearly sunspot numbers while both stationary and nonstationary linear stochastic models are entertained for modeling a tree ring series. Three types of models are considered for modeling seasonal time series and in a practical application it is shown how to select the most appropriate model to fit to a monthly river flow time series. The model building procedure is also used to select an intervention model that describes the effects of the Aswan Dam on the average annual flows of the Nile River.