摘要：
Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making.
期刊：
Information Processing & Management,2023年60(4):103348 ISSN：0306-4573
通讯作者：
Duantengchuan Li<&wdkj&>Yan Zhang
作者机构：
[Li, Zhifei; Zhang, Yan] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Hubei, Peoples R China.;[Zhang, Qi] Cent China Normal Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.;[Zhu, Fangfang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Hubei, Peoples R China.;[Zheng, Chao; Li, Duantengchuan] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.
通讯机构：
[Duantengchuan Li; Yan Zhang] S;School of Computer Science, Wuhan University, Wuhan, Hubei 430072, China<&wdkj&>School of Computer Science and Information Engineering, Hubei University, Wuhan, Hubei 430062, China
作者机构：
[Cao, Shiyang] Shanxi Univ Finance & Econ, Int Exchange & Cooperat Dept, Taiyuan, Peoples R China.;[Ma, Xiao] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.;[Yi, Ming; Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Zeng, Jiangfeng] Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
通讯机构：
[Zeng, JF ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
关键词：
Financial sentiment analysis;Fresh and hot opinions;Temporal modeling;Fresh-hot bilinear pooling
摘要：
Financial sentiment analysis aims to extract public opinion about an institution to help financial researchers make better decisions. To predict sentiment more accurately, it is necessary for models to improve their capability to capture long-term temporal information and support multi-user interaction. However, existing methods only analyze sentiment based on one comment from a user, which fails to fully exploit the latent emotions of the public, and they lack effective temporal modeling and interaction capabilities. In this paper, we analyze a company from two perspectives to alleviate the above issues: (1) the fresh opinions can reflect timely public attitudes towards a company, while (2) the hot opinions provide the most influential views. A comprehensive exploration of fresh and hot financial sentiment can help researchers make more accurate determinations. To this end, we propose a novel financial sentiment classification framework (FSCN), that can capture temporal information and interact with the opinions of users to make a more comprehensive decision. Our approach takes into account the inherent temporal dependencies in public opinions and combines both views of information to achieve an accurate classification of financial sentiment. Specifically, the FSCN contains (1) a multi-opinion extractor to filter and extract features from massively fresh and hot opinions, respectively. (2) a fresh-hot bilinear pooling (FHBP) module to effectively fuse fresh and hot features. Additionally, to verify the effectiveness of the proposed method, we crawl data from the Internet and create a real-world public opinion dataset that consists of 79,350 comments from 837 companies. Extensive experiments demonstrate that our framework achieves state-of-the-art results on this real-world dataset and is capable of providing reliable service in the financial system. Codes will be released at https://github.com/zjfgh2015/FSCN .
