Title: Methanol price prediction method based on multimodal fusion by using CNN-GRU and attention mechanism

Authors: Shuang Luo; Xuhui Zhu; Zhiwei Ni; Pingfan Xia; Liping Ni

Addresses: School of Management, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Decision-Making and Information System Technology, Engineering Research Center of Ministry of Education, Hefei 230009, Anhui, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, Anhui, China ' School of Management, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Decision-Making and Information System Technology, Engineering Research Center of Ministry of Education, Hefei 230009, Anhui, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, Anhui, China ' School of Management, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Decision-Making and Information System Technology, Engineering Research Center of Ministry of Education, Hefei 230009, Anhui, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, Anhui, China ' School of Management, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Decision-Making and Information System Technology, Engineering Research Center of Ministry of Education, Hefei 230009, Anhui, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, Anhui, China ' School of Management, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China; Intelligent Decision-Making and Information System Technology, Engineering Research Center of Ministry of Education, Hefei 230009, Anhui, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, Anhui, China

Abstract: Considering that text is important to methanol price prediction, the text and the quantity information need to be fused for achieving high precision prediction. Hence, we put forward a novel methanol price forecasting approach ground on 'text-quantity' multimodal fusion using CNN-GRU-attention mechanism network, named MFCGAM, which merges quantity information and text information obtained from 'research report', 'information' and 'investor comments'. Firstly, Word2Vec model is applied to process text, and the 'text-quantity' dual channel based on CNN and GRU is established to extract text and quantity features respectively. Secondly, attention mechanism is employed to get 'text-quantity' fused characteristics, which are used to predict methanol price. The experimental outcomes of three real datasets show that MFCGAM model obtains superior performance than other traditional models. Additionally, predictive ability of models can be improved by adding texts, and it is found that the results of short-term prediction are better than that those of long-term forecasting when using texts. It provides a very useful predictive tool for smart scheduling of coking intelligent plants.

Keywords: methanal price prediction; multimodal fusion; attention mechanism; gated recurrent unit; GRU; convolutional neural network; CNN.

DOI: 10.1504/IJBIC.2025.143660

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.1, pp.43 - 55

Received: 06 Jul 2022
Accepted: 28 Jan 2023
Published online: 03 Jan 2025 *

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