A hybrid VMD–BiGRU model for rubber futures time series forecasting

Zhu, Qing; Zhang, Fan; Liu, Shan; Wu, Yiqiong; Wang, Lin

doi:10.1016/j.asoc.2019.105739

Cited by 92 publications

(36 citation statements)

References 42 publications

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“…In the BiGRU, two GRU inputs in opposite directions are provided at the same time at each time t. GRUs in the two directions are not directly connected, and the output is jointly determined by two unidirectional GRUs. e BiGRU model has good prediction performance in nonlinear time series data [24,25]. e structure of the BiGRU is shown in Figure 3.…”

Section: Scientific Programmingmentioning

confidence: 99%

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Luo

et al. 2021

Scientific Programming

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In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.

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Section: Scientific Programmingmentioning

confidence: 99%

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Luo

et al. 2021

Scientific Programming

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“…Globally, energy, agricultural, and industrial metal products markets are complex, uncertain, volatile, and interdependent. As a result, crude oil, corn, and gold time series are nonlinear and nonstationary [5]. erefore, determining the drivers of these commodities are demanding task [6].…”

Section: Related Studiesmentioning

confidence: 99%

“…Zhu et al [5] analyzed the price series and volatility of natural rubber's market using VMD as data preprocessing to decompose the rubber futures series from Shanghai Future Exchange into different modes. A hybrid VMD-BiGRU model was formulated to forecast the short-term rubber futures on the Shanghai Future Exchange.…”

Section: Related Studiesmentioning

confidence: 99%

Determinants of Commodity Futures Prices: Decomposition Approach

Antwi

Gyamfi

Kyei

et al. 2021

Mathematical Problems in Engineering

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Developing models to analyze time series is a very sophisticated, time-consuming, but interesting experience for researchers. Commodity price component determination is challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. This study aims to determine the components that drive the market price of commodity futures. This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products, crude oil from energy, and gold from industrial metal. We applied these techniques to decompose the daily data of each commodity price from different periods and frequencies into individual intrinsic mode functions for EMD and modes for VMD. We used the hierarchical clustering method and Euclidean distance approach to classify the IMFs and modes into high-frequency, low-frequency, and trend. Next, applying statistical measures, particularly, the Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient, we observed that the trend and low-frequency parts of the market price are the main drivers of commodity futures markets’ price fluctuations. The low-frequencies are caused by special events. In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.

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“…Zhang et al [47] proposed a hybrid approach which combines EEMD and LSTM for daily land surface temperature forecasting to reduce the difficulty of modeling and to improve prediction accuracy. Zhu et al [48] proposed VMD-BiGRU for rubber futures time series forecasting, in which Variational Mode Decomposition (VMD) is utilized to capture the tendency and mutability information of time series, and BiGRU is to make one-dayahead prediction. All of these works have proved that hybrid models applying decomposition technology can achieve better performance.…”

Section: A Related Workmentioning

confidence: 99%

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

Liu

Cai

2020

IEEE Access

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Multivariate Time Series Forecasting (MTSF) has recently emerged its growing importance in many industries. However, how to reduce the influence of the noise components existing in time series on prediction and extract features effectively are still two challenges in MTSF. This paper focuses on those two challenges and proposes a new prediction method based on decomposition-ensemble framework called adaptive sub-series clustering-stacked residual LSTMs-multi-level attention mechanism (ASC-SRLSTMs-MLAttn). The method consists of three stages: decomposition, prediction, and ensemble. In the decomposition stage, the target series is decomposed by Ensemble Empirical Mode Decomposition into multiple sub-series, which will be clustered and reconstructed by the ASC algorithm to reduce the complexity and the time consumption of prediction. In the prediction stage, the sub-series and correlation series will be fed into SRLSTMs-MLAttn for sub-series prediction. The model is based on the encoder-decoder architecture with stacked residual LSTMs as the encoder, which can effectively capture the dependencies among multi variables and the temporal features from multivariate time series. Besides, a multi-level attention mechanism (MLAttn), which makes full use of the encoding information of the encoder, has been introduced to further improve the prediction performance of the model. In the ensemble stage, the predicted values of each sub-series will be summed to obtain the final prediction of the target time series. We also demonstrate the superiority and effectiveness of our proposed method on four public datasets via the conducted comparison experiment and ablation study. INDEX TERMS Multivariate time series forecasting, decomposition-ensemble framework, adaptive sub-series clustering algorithm, stacked residual long short-term memory, multi-level attention mechanism.

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A hybrid VMD–BiGRU model for rubber futures time series forecasting

Cited by 92 publications

References 42 publications

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Determinants of Commodity Futures Prices: Decomposition Approach

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

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