An Anomaly Recognition Algorithm for Financial Data based on Self-Organizing Fuzzy Rule

DOI

10.2991/mmebc-16.2016.427How to use a DOI?

Keywords

Self-Organizing Fuzzy Rule; Financial Data; Anomaly Recognition

Abstract

Financial data has the characteristic of nonstationary, nonlinear and low SNR. Due to the lack of financial data anomalies training set, which results in greater difficulties in the intelligent algorithm on financial data anomaly recognition. Therefore, an anomaly recognition algorithm for financial data based on self-organizing fuzzy rule is proposed in this paper. The financial transaction data is generated by the time sequence of the time span of the week, and then select the total amount of the transaction, the discrete factor of the transaction, the number of transfer as the characteristics of the financial account data. The validity of the method is illustrated by the experimental data.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuebing Feng
PY  - 2016/06
DA  - 2016/06
TI  - An Anomaly Recognition Algorithm for Financial Data based on Self-Organizing Fuzzy Rule
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 2137
EP  - 2140
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.427
DO  - 10.2991/mmebc-16.2016.427
ID  - Feng2016/06
ER  -