Performance analysis of four decomposition-ensemble models for one-day-ahead agricultural commodity futures price forecasting
type: Item
instance of: scholarly article
title: Performance analysis of four decomposition-ensemble models for one-day-ahead agricultural commodity futures price forecasting
zbMATH Open document ID: 1461.91209
author: De-Yun Wang
author: Chenqiang Yue
author: Shuai Wei
author: J. Lv
published in: Algorithms
publication date: 2018-08-13T00:00:00Z
review text: Summary: Agricultural commodity futures prices play a significant role in the change tendency of these spot prices and the supply-demand relationship of global agricultural product markets. Due to the nonlinear and nonstationary nature of this kind of time series data, it is inevitable for price forecasting research to take this nature into consideration. Therefore, we aim to enrich the existing research literature and offer a new way of thinking about forecasting agricultural commodity futures prices, so that four hybrid models are proposed based on the back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD). In order to verify the applicability and validity of these hybrid models, we select three futures prices of wheat, corn and soybean to conduct the experiment. The experimental results show that (1) all the hybrid models combined with decomposition technique have a better performance than the single PSO-BPNN model; (2) VMD contributes the most in improving the forecasting ability of the PSO-BPNN model, while WPT ranks second; (3) ITD performs better than EMD in both cases of corn and soybean; and (4) the proposed models perform well in the forecasting of agricultural commodity futures prices.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 6916767
zbMATH Keywords: agricultural commodity futures prices
zbMATH Keywords: back propagation neural network (BPNN)
zbMATH Keywords: particle swarm optimization (PSO)
zbMATH Keywords: decomposition methods
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a10030108
OpenAlex ID: W2755418557
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DOI: 10.3390/A10030108
description: scientific article; zbMATH DE number 6916767