A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting

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)...

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Veröffentlicht in:Energy (Oxford) Jg. 157; S. 526 - 538
Hauptverfasser: Tang, Ling, Wu, Yao, Yu, Lean
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.08.2018
Elsevier BV
Schlagworte:
Algorithms
artificial intelligence
Big Data
Computer applications
Computing time
Crude oil
Decomposition
Decomposition-ensemble learning methodology
Econometrics
energy costs
Energy price forecasting
Ensemble learning
Extreme learning machine
Forecasting
Intelligence
Learning algorithms
Machine learning
Mathematical models
Natural gas
Neural networks
oils
Parameter sensitivity
prediction
prices
Random kitchen sinks
Random vector functional link network
Randomization
Randomized algorithm
Randomized algorithms
Statistical analysis
Statistical methods
Random kitchen sinks
Extreme learning machine
Randomized algorithm
Random vector functional link network
Energy price forecasting
Decomposition-ensemble learning methodology
ISSN:0360-5442, 1873-6785
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Zusammenfassung: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). •A randomized-algorithm-based decomposition-ensemble learning method is proposed.•Randomized algorithms are used to construct extremely fast individual predictors.•The proposed method outperforms popular single methods and ensemble variants in accuracy.•EEMD-RVFL is extremely efficient and fast technique for energy price forecasting.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2018.05.146