A combined neural network model for commodity price forecasting with SSA

Commodity price forecasting is challenging full of volatility, uncertainty and complexity. In this paper, a novel modeling framework is proposed to predict the market price of commodity futures. Three types of commodity are selected as representatives: corn from agricultural products, gold from indu...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2018-08, Vol.22 (16), p.5323-5333
Main Authors: Wang, Jue, Li, Xiang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Back propagation
Back propagation networks
Causality
Commodities
Commodity futures
Commodity prices
Computational Intelligence
Control
Engineering
Focus
Forecasting
Futures market
Futures trading
Gold
Hypotheses
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Neural networks
Propagation
Radial basis function
Robotics
Spectrum analysis
Time series
Volatility
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Summary:Commodity price forecasting is challenging full of volatility, uncertainty and complexity. In this paper, a novel modeling framework is proposed to predict the market price of commodity futures. Three types of commodity are selected as representatives: corn from agricultural products, gold from industrial metal and crude oil from energy. We decomposed the original series into independent components at various scales using singular spectrum analysis (SSA). A SSA-causality test is introduced to investigate the mutual influence between commodity futures prices. Additionally, using the SSA-smoothing scheme, we construct combined neural network models including back propagation, radial basis function and wavelet neural network to predict the commodity price. The experimental results illustrate that neural network models with the SSA outperform the benchmarks in terms of distinct measures.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3023-2