sequential_reweighting

list of pairwise similarity values. See
<code><a rd-options="" href="/link/sliding_similarity?package=tsensembler&version=0.0.5" data-mini-rdoc="tsensembler::sliding_similarity">sliding_similarity</a></code>

sliding_similarity

Besides ensemble heterogeneity we encourage diversity
explicitly during the aggregation of the output of experts.
This is achieved by taking into account not only predictions
of performance produced by the arbiters, but also the
correlation among experts in a recent window of observations.

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

sequential_reweighting function

list of pairwise similarity values. See
<code><a rd-options='' href='sliding_similarity'>sliding_similarity</a></code>

sequential_reweighting: Sequential Re-weighting for controlling predictions' redundancy

Description

Usage

Arguments