bm_gaussianprocess

form

training data for building the predictive
model

data

a list containing the learning parameters

lpars

Learning a Gaussian Process model from training
data. Parameter setting can vary in kernel
and tolerance. See <code><a rd-options="kernlab" href="/link/gausspr?package=tsensembler&version=0.0.5&to=kernlab" data-mini-rdoc="kernlab::gausspr">gausspr</a></code>
for a comprehensive description.

internal

A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.

Vitor Cerqueira

tsensembler

Dynamic Ensembles for Time Series Forecasting

Luis Torgo

Carlos Soares

bm_gaussianprocess function

Learning a Gaussian Process model from training
data. Parameter setting can vary in kernel
and tolerance. See <code><a rd-options='kernlab' href='gausspr'>gausspr</a></code>
for a comprehensive description.

bm_gaussianprocess: Fit Gaussian Process models

Description

Usage

Arguments

Value

Details

See Also