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

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 716-721. DOI: 10.5220/0010900500003122

in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2022},
pages={716-721},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010900500003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Exploring Enterprise Operating Indicator Data by Hierarchical Forecasting and Root Cause Analysis
SN - 978-989-758-549-4
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