water_consumption

A time series of classes <code>xts</code> and <code>zoo</code>
containing the water consumption
levels a specific delivery point at Oporto town, in
Portugal.

datasets

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

water_consumption function

The time series has 1741 values from Jan, 2012 to Oct,
2016 in a daily granularity.<dl class="dl-horizontal">
 <dt>consumption</dt><dd>consumption of water, raw value from sensor</dd>
</dl>

Format

Water Consumption in Oporto city (Portugal) area. — water_consumption

The time series has 1741 values from Jan, 2012 to Oct,
2016 in a daily granularity.<dl class='dl-horizontal'>
 <dt>consumption</dt><dd>consumption of water, raw value from sensor</dd>
</dl>

water_consumption: Water Consumption in Oporto city (Portugal) area.

Description

Usage

Arguments

Format