split_by

split_by_

split_by.

expr

expression to split <code>expr</code> by;

split

Logical. If TRUE, the splitted <code>expr</code> is unlisted;

unlist.

Further parameters to pass to <code>strsplit</code>;

This is an utility function that can be used to split expressions.
It is based on <code>strsplit</code> function.
split_by is the general purpose splitter
split_by_ splits expressions by \"_\"
split_by. splits expressions by a dot

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>.

split_by: Splitting expressions by pattern

Description

Usage

Arguments

Value

Examples