A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion

Li, Xiaohan; Wang, Jun; Tan, Jinghua; Ji, Shiyu; Jia, Huading

doi:10.1007/s11042-022-13231-1

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…We intend to apply this CNN architecture in other databases with similar characteristics and also compare it with other techniques, such as Graph Neural Network methods, which have recently been applied to financial data in different contexts (Li et al, 2020; Li, Wang, et al, 2022; Wang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural networks applied to data organized as OLAP cubes

Caputo,

dos Santos,

da Rocha

2023

Expert Systems

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This paper presents a Convolutional Neural Network (CNN) architecture named OlapNet, which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data on the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modelled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural networks applied to data organized as OLAP cubes

Caputo,

dos Santos,

da Rocha

2023

Expert Systems

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“…Reasoning is the process of deriving corresponding conclusions from existing objective facts under specific rules. Fuzzy reasoning technology, which combines fuzzy theory and reasoning process, has been widely used to deal with fuzzy problems (Li et al, 2022).…”

Section: ) Fuzzy Reasoning Methodsmentioning

confidence: 99%

Multi-source fusion of substation intelligent inspection robot based on knowledge graph: A overview and roadmap

Huang

et al. 2022

Front. Energy Res.

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As the core part of the power grid, the substation must carry out regular patrol inspection to ensure the normal operation of the equipment. In recent years, with the continuous increase of load, the area of the substation has gradually expanded, which makes its operating environment more complex, the intensity of patrol inspection has increased, and the manual patrol inspection method cannot meet the needs of patrol inspection. Therefore, it is necessary to introduce high-tech equipment and use patrol inspection robots to conduct regular patrol inspection, which inevitably leads to a large number of different working environments with matching standards and regulations for different equipment. In order to facilitate the staff to implement the delivery of robots matching the standards for the projects in the corresponding actual environment, the concept of knowledge atlas has been introduced and revolutionarily applied to the multi-source fusion of substation patrol robots, which not only more clearly analyzes the relationship between multi-source data, which can also make the operation and application in practical projects more convenient. This paper creatively uses the method of knowledge graph to summarize and analyze the multi-source fusion data of the field patrol robot in different working environments of a substation in Southwest China, and provides the research direction of this field and a one-stop manual for potential researchers and engineers in the future.

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“…Additionally, research has explored combining multi-source data, graphical neural networks, and extended hidden Markov models to improve the accuracy of stock market predictions. Fusion techniques, including information fusion, feature fusion, and model fusion, have been investigated to improve prediction models [27][18] [29]. Synthesis of various advanced techniques such as deep learning, transfer learning, and combining heterogeneous data sources can significantly contribute to the development of powerful models for predicting stock market movements.…”

Section: Data Collectionmentioning

confidence: 99%

Implications of Deep Learning for Stock Market Forecasting

Supendi,

Kumala,

Yulianti

2024

ijsecs

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This research explores the effectiveness of using deep learning in predicting stock market movements. This research uses rigorous methods to bring out the performance of deep learning models, compare them with traditional methods, and identify critical factors that influence stock market predictions. The research results show that deep learning models, especially LSTM and CNN-LSTM architectures, can achieve satisfactory levels of accuracy and outperform traditional methods by capturing patterns in complex stock market data. In addition, this research identifies external and internal factors that influence predictions of stock market movements. This research's practical and theoretical implications highlight the potential of deep learning in improving investment decision-making and understanding financial market dynamics. Recommendations for future research include exploration of advanced deep learning techniques, integration with traditional methods, emphasis on risk management strategies, continuous evaluation of model performance, and provision of training and education to encourage analysts and investors to adopt this technology. By implementing these recommendations, the potential of deep learning models in financial analysis can be optimized, ultimately improving market efficiency and investment returns.

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A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion

Cited by 16 publications

References 33 publications

Convolutional neural networks applied to data organized as OLAP cubes

Convolutional neural networks applied to data organized as OLAP cubes

Multi-source fusion of substation intelligent inspection robot based on knowledge graph: A overview and roadmap

Implications of Deep Learning for Stock Market Forecasting

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