embed_timeseries

timeseries

an integer specifying the embedding dimension.

embedding.dimension

This function embeds a time series into an Euclidean space.
This implementation is based on the function <code>embed</code> of
stats package and has theoretical backgroung on
reconstruction of attractors (see Takens, 1981).
This shape transformation of the series allows for
the use of any regression tool available to learn
the time series. The assumption is that there are no long-term
dependencies in the data.

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>.

embed_timeseries: Embedding a Time Series

Description

Usage

Arguments

Value

See Also

Examples