CA.ADE_hat

a <code><a rd-options="" href="/link/constructive_aggregation-class?package=tsensembler&version=0.0.5" data-mini-rdoc="tsensembler::constructive_aggregation-class">constructive_aggregation-class</a></code>
object class

predict information -- output
of <code><a rd-options="" href="/link/hat_info?package=tsensembler&version=0.0.5" data-mini-rdoc="tsensembler::hat_info">hat_info</a></code> function

predict_info

newdata

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

CA.ADE_hat function

a <code><a rd-options='' href='constructive_aggregation-class'>constructive_aggregation-class</a></code>
object class

predict information -- output
of <code><a rd-options='' href='hat_info'>hat_info</a></code> function

CA.ADE_hat: CA generaliser using arbitrage

Description

Usage

Arguments