Outlier detection of financial high frequency data based on fuzzy clustering algorithm

Rui Dai

doi:10.1117/12.2642795

28 July 2022 Outlier detection of financial high frequency data based on fuzzy clustering algorithm

Rui Dai

Proceedings Volume 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022); 1230308 (2022) https://doi.org/10.1117/12.2642795
Event: International Conference on Cloud Computing, Internet of Things, and Computer Applications, 2022, Luoyang, China

Abstract

Abnormal intraday fluctuations in financial markets may be related to major economic events. Therefore, by analyzing the relationship between the anomalies of high-frequency market data and the attributes of market internal events, market participants can have a deeper understanding of the dynamic characteristics of the market. As a new data model, uncertain data is widely used in finance, location-based services, mobile object monitoring, sensor networks and many other types of applications. In fact, cluster analysis is a data processing process, which uses the characteristic attributes between data and adopts some reasonable similarity index to measure, and finally obtains a mathematical classification result. As for the clustering results, we require that the data object gap in the same cluster should be as small as possible, while the data gap in different groups should be as large as possible, while the data gap in different groups be as large as possible. Differences between data objects are usually defined by attributes of data objects, for example, the distance between data can be defined for numeric attributes.

Citation Download Citation

Rui Dai "Outlier detection of financial high frequency data based on fuzzy clustering algorithm", Proc. SPIE 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), 1230308 (28 July 2022); https://doi.org/10.1117/12.2642795

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