EMASE

Squared error of the models at each test point;

loss

Number of periods to average over when computing MASE;

lambda

pre-weights of the base models computed in the
train set.

pre_weights

This function computes the weights of the learning models
using the Moving Average Squared Error (MASE) function
This method provides a simple way to quantify the recent
performance of each base learner and adapt the combined
model accordingly.

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>.

EMASE: Weighting Base Models by their Moving Average Squared Error

Description

Usage

Arguments

Value

See Also