平滑的
广义加性模型
计算机科学
网格
花键(机械)
数学优化
平滑样条曲线
残余物
算法
加性模型
数据集
集合(抽象数据类型)
数据挖掘
数学
机器学习
人工智能
工程类
几何学
结构工程
计算机视觉
程序设计语言
样条插值
双线性插值
作者
Simon N. Wood,Yannig Goude,Simon Shaw
摘要
Summary We consider an application in electricity grid load prediction, where generalized additive models are appropriate, but where the data set's size can make their use practically intractable with existing methods. We therefore develop practical generalized additive model fitting methods for large data sets in the case in which the smooth terms in the model are represented by using penalized regression splines. The methods use iterative update schemes to obtain factors of the model matrix while requiring only subblocks of the model matrix to be computed at any one time. We show that efficient smoothing parameter estimation can be carried out in a well-justified manner. The grid load prediction problem requires updates of the model fit, as new data become available, and some means for dealing with residual auto-correlation in grid load. Methods are provided for these problems and parallel implementation is covered. The methods allow estimation of generalized additive models for large data sets by using modest computer hardware, and the grid load prediction problem illustrates the utility of reduced rank spline smoothing methods for dealing with complex modelling problems.
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