model_weighting

A object describing the loss of each base model

Character value describing the transformation type.
The available options are softmax, linear and
erfc. The softmax and erfc provide a non-linear transformation
where the weights decay exponentially as the relative loss of a given model
increases (with respect to all available models). The linear transformation
is a simple normalization of values using the max-min method.

trans

Further arguments to <code>normalize</code> and
<code>proportion</code> functions \(na.rm = TRUE\)

This is an utility function that takes the raw error of
models and scales them into a 0-1 range according to one
of three strategies:

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>.

model_weighting: Model weighting

Description

Usage

Arguments

Value

Details

See Also