Anomaly detection method of regional economy big data based on time series algorithm

Ying Liu; Zulkefly Abdul Karim; Norlin Khalid; Fathin Faizah Said

doi:10.1117/12.2646334

23 August 2022 Anomaly detection method of regional economy big data based on time series algorithm

Ying Liu, Zulkefly Abdul Karim, Norlin Khalid, Fathin Faizah Said

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123300Z (2022) https://doi.org/10.1117/12.2646334
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

In order to solve the problem of high false positive rate of regional economic big data anomaly detection, a regional economic big data anomaly detection method based on time series algorithm is designed. Select regional economicindicators and divide socio-economic statistics and spatial data; Optimize the data space conversion process, select influencing factors and modeling methods; The simultaneous equation model is established by using time seriesalgorithm, and the anomaly detection mode of big data is established to realize the anomaly detection of regional economic big data. Experimental results: the average false positive rates of the regional economic big data anomalydetection method proposed in this paper and the other two regional economic big data anomaly detection methods are2.477%, 4.060% and 3.986% respectively, indicating that the detection method designed in this paper has lowfalsepositive rate and better detection performance.

Citation Download Citation

Ying Liu, Zulkefly Abdul Karim, Norlin Khalid, and Fathin Faizah Said "Anomaly detection method of regional economy big data based on time series algorithm", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123300Z (23 August 2022); https://doi.org/10.1117/12.2646334

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