Financial time series forecasting with multi-modality graph neural network

Cheng, Dawei; Yang, Fangzhou; Xiang, Sheng; Liu, Jin

doi:10.1016/j.patcog.2021.108218

Cited by 161 publications

(46 citation statements)

References 22 publications

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“…The emergence of Graph Neural Networks (GNNs) [63] has revolutionized deep learning on graph-structured data. GNNs have achieved superior performances across many fields such as social network analysis [101,9], biochemistry [150], computer vision [66] and finance [23]. Despite their great success, GNNs are generally treated as black-box since their decisions are less understood [145,79], leading to the increasing concerns about the explainability of GNNs.…”

Section: Explainabilitymentioning

confidence: 99%

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Wu¹,

Li²,

Yu³

et al. 2022

Preprint

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Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerging problem, especially in risk-sensitive domains. Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. In contrast to conventional graph learning research which mainly cares about model performance, TwGL considers various reliability and safety aspects of the graph

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Section: Explainabilitymentioning

confidence: 99%

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Wu¹,

Li²,

Yu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Consequently, deep leaning is practiced in probabilistic wind power forecasting with highaccuracy and consistency [30]. In [31], a multi-modality graph neural network is proposed addressing the major issues in price prediction in the financial industry. The proposed model was used to learn the lead-lag effects of financial time series.…”

Section: Forecasting Modelsmentioning

confidence: 99%

False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting

et al. 2022

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Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate.

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“…They propose a multimodal graph neural network (MAGNN) to learn from these multimodal inputs for financial risk prediction. They also build an intelligent Q&A system based on enterprise risk KG, which helps discover dynamic and sudden financial risks [43,44]. In healthcare, KG can assist in performing risk assessment of miscarriage before and during pregnancy [45].…”

Section: Kg and Its Applications In The Risk Fieldmentioning

confidence: 99%

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Wang

Zhang

Feng

et al. 2021

IJGI

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The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.

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Financial time series forecasting with multi-modality graph neural network

Cited by 161 publications

References 22 publications

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

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