A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting
Ling Tang,
Yao Wu and
Lean Yu ()
Energy, 2018, vol. 157, issue C, 526-538
Abstract:
Inspired by the interesting idea of randomization, some powerful but time-consuming decomposition-ensemble learning paradigms can be extended into extremely efficient and fast variants by using randomized algorithms as individual forecasting tools. In the proposed methodology, Three major steps, (1) data decomposition via ensemble empirical mode decomposition, (2) individual prediction via a randomized algorithm (using randomization to mitigate training time and parameter sensitivity), and (3) results ensemble to produce final prediction, are included. Different from other existing decomposition-ensemble models using traditional econometric approaches or computational intelligence methods in individual prediction, this study employs some emerging randomized algorithms—extreme learning machine, random vector functional link network (using randomly fixed weights and bias in neural networks), and random kitchen sinks (using randomly mapping features to approximate kernels)—to dramatically save computational time and enhance prediction accuracy. With the Brent oil prices and the Henry Hub natural gas prices as studying samples, the empirical study statistically confirms that the proposed randomized-algorithm-based decomposition-ensemble learning models are proved to be excellently efficient and fast, relative to popular single techniques (including computational intelligence methods and randomized algorithms) and similar decomposition-ensemble counterparts (using the aforementioned single techniques as individual forecasting tools).
Keywords: Decomposition-ensemble learning methodology; Randomized algorithm; Energy price forecasting; Extreme learning machine; Random vector functional link network; Random kitchen sinks (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218309800
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:157:y:2018:i:c:p:526-538
DOI: 10.1016/j.energy.2018.05.146
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().