Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis

Yue Pang; Yue Pang; Jing Pan; Xiaogang Li; Jianbin Zheng; Tan Sun; Qinxin Li

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Paper

Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis

Topics: Data Mining and Algorithms for Big Data; Industry Related Applications

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 716-721, 2022

Authors: Yue Pang ^{1

;

2} ; Jing Pan ¹ ; Xiaogang Li ¹ ; Jianbin Zheng ¹ ; Tan Sun ¹ and Qinxin Li ¹

Affiliations: ¹ China UnionPay Co., Ltd., Shanghai 201201, China ; ² School of Computer Science, Fudan University, Shanghai 200433, China

Keyword(s): Time Series, Hierarchical Forecasting, Root Cause Analysis.

Abstract: Enterprise operating indicators analysis is essential for the decision maker to grasp the situation of enterprise operation. In this work, time series prediction and root cause analysis algorithms are adopted to form a multi-dimensional analysis method, which is used to accurately and rapidly locate enterprise operational anomaly. The method is conducted on real operating indicator data from a financial technology company, and the experimental results validate the effectiveness of multi-dimensional analysis method.

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Paper citation in several formats:

Pang, Y.; Pan, J.; Li, X.; Zheng, J.; Sun, T. and Li, Q. (2022). Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 716-721. DOI: 10.5220/0010900500003122

@conference{icpram22,
author={Yue Pang. and Jing Pan. and Xiaogang Li. and Jianbin Zheng. and Tan Sun. and Qinxin Li.},
title={Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={716-721},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010900500003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis
SN - 978-989-758-549-4
IS - 2184-4313
AU - Pang, Y.
AU - Pan, J.
AU - Li, X.
AU - Zheng, J.
AU - Sun, T.
AU - Li, Q.
PY - 2022
SP - 716
EP - 721
DO - 10.5220/0010900500003122
PB - SciTePress