结合 TCN-BiGRU 和关注机制的电池级碳酸锂混合价格预测方法

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS
Kybernetes Pub Date : 2024-09-12 DOI:10.1108/k-05-2024-1228
Zhanglin Peng, Tianci Yin, Xuhui Zhu, Xiaonong Lu, Xiaoyu Li
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引用次数: 0

摘要

目的为了准确预测电池级碳酸锂的价格,为投资者提供正确的指导,本研究提出了一种名为 MFTBGAM 的方法。该方法利用 TCN-BiGRU-Attention 将文本信息和数字信息整合在一起。设计/方法/方法首先使用 Word2Vec 模型处理收集到的有关电池级碳酸锂的文本数据。然后,构建一个整合了 TCN 和 BiGRU 的双通道文本-数字提取模型,分别提取文本和数字特征。然后,应用注意力机制从文本和数字数据中提取融合特征。最后,使用全连接层计算并输出电池级碳酸锂的市场价格预测结果。 研究结果本研究使用新闻和投资者评论组成的数据集进行了实验。研究限制/意义本研究分析的数据集跨越 2020 年至 2023 年,因此,预测结果与该时间段特别相关。更改样本数据将需要重复实验过程,从而导致不同的结果。此外,考虑到原始数据可能包含噪音和无关信息,未来的工作将探索有效的数据预处理技术,以减少此类问题,从而提高模型在长期预测任务中的预测能力。 社会意义该价格预测模型是电池级碳酸锂行业投资者的重要工具,有助于做出明智的投资决策。利用价格预测的结果,投资者可以辨别投资时机。此外,本研究利用两种不同类型的文本信息--新闻和投资者评论--作为独立的文本数据输入源。原创性/价值我们提出了一种基于 TCN-BiGRU 注意力的 "文本-数字 "信息融合的新型价格预测方法。我们分别使用新闻和投资者评论两种文本信息进行预测,以提高模型的有效性和泛化能力。此外,我们还利用包括标题和内容在内的新闻数据集来提高电池级碳酸锂市场价格预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid price prediction method combining TCN-BiGRU and attention mechanism for battery-grade lithium carbonate

Purpose

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

Design/methodology/approach

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Findings

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

Research limitations/implications

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

Social implications

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

Originality/value

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

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来源期刊
Kybernetes
Kybernetes 工程技术-计算机:控制论
CiteScore
4.90
自引率
16.00%
发文量
237
审稿时长
4.3 months
期刊介绍: Kybernetes is the official journal of the UNESCO recognized World Organisation of Systems and Cybernetics (WOSC), and The Cybernetics Society. The journal is an important forum for the exchange of knowledge and information among all those who are interested in cybernetics and systems thinking. It is devoted to improvement in the understanding of human, social, organizational, technological and sustainable aspects of society and their interdependencies. It encourages consideration of a range of theories, methodologies and approaches, and their transdisciplinary links. The spirit of the journal comes from Norbert Wiener''s understanding of cybernetics as "The Human Use of Human Beings." Hence, Kybernetes strives for examination and analysis, based on a systemic frame of reference, of burning issues of ecosystems, society, organizations, businesses and human behavior.
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