Trading Outlier Detection: Machine Learning Approach
| Author(s) | : | Nitin Ghatage, Prof. Prashant Ahitre |
| Institution | : | Computer Engineering, Dr. D.Y. Patil Institute of Technology Pimpri, Pune-411018 |
| Published In | : | Vol. 6, Issue 12 — December 2019 |
| Page No. | : | 61-65 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Anomaly detection is usually associate degree identification of associate degree odd or abnormalinformation typically even known as as an outlier from a offer pattern of information. It involves machine learningtechnique to be told the info and verify the outliers supported a likelihood condition. Machine learning, a branchof AI plays a significant role in analyzing the info and identifies the outliers with a decent likelihood. The target of thispaper is to work out the outlier supported anomaly detection techniques and describe the quality standards of the actualtrade. We have a tendency to describe associate degree approach to analyzing anomalies in trade informationsupported the identification of cluster outliers.
Nitin Ghatage, Prof. Prashant Ahitre, “Trading Outlier Detection: Machine Learning Approach”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 6, Issue 12, pp. 61-65, December 2019.
