erfc

A numeric vector. The default value for the parameter
<code>lambda</code> presumes that <code>x</code> is in a 0--1 range. In the scope of
this package, this is achieved using normalize function;

parameter used to control the flatness of the erfc curve.
Defaults to 2.

alpha

Erfc stands for the Complementary Gaussian Error Function.
This mathematical formula can be used as a squashing function.
Consider <code>x</code> a numeric vector representing the squared error of
base models in a given observation. By applying the erfc function on
the error, the weight of a given model decays exponentially as its
loss increases.

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>.

erfc: Complementary Gaussian Error Function

Description

Usage

Arguments

Value

References

Examples