tempGP {DSWE}R Documentation

temporal Gaussian process

Description

A Gaussian process based power curve model which explicitly models the temporal aspect of the power curve. The model consists of two parts: f(x) and g(t).

Usage

tempGP(trainX, trainY, trainT = NULL)

Arguments

trainX

A matrix with each column corresponding to one input variable.

trainY

A vector with each element corresponding to the output at the corresponding row of trainX.

trainT

A vector for time indices of the data points. By default, the function assigns natural numbers starting from 1 as the time indices.

Value

An object of class tempGP with the following attributes:

References

Prakash, A., Tuo, R., & Ding, Y. (2020). "The temporal overfitting problem with applications in wind power curve modeling." arXiv preprint arXiv:2012.01349. <https://arxiv.org/abs/2012.01349>.

See Also

predict.tempGP for computing predictions and updateData.tempGP for updating data in a tempGP object.

Examples


    data = DSWE::data1
    trainindex = 1:100 #using the first 100 data points to train the model
    traindata = data[trainindex,]
    xCol = 2 #input variable columns
    yCol = 7 #response column
    trainX = as.matrix(traindata[,xCol])
    trainY = as.numeric(traindata[,yCol])
    tempGPObject = tempGP(trainX, trainY)



[Package DSWE version 1.5.1 Index]