Authors: Merih Bozbura 1 ; Hunkar Tunc 2 ; Miray Kusak 1 and C. Sakar 3

Affiliations: 1 Inveon Digital Commerce Solutions Limited, Istanbul and Turkey ; 2 Department of Computer and Information Science, University of Konstanz, Konstanz and Germany ; 3 Department of Computer Engineering, Bahcesehir University, Istanbul and Turkey

ISBN: 978-989-758-377-3

ISSN: 2184-285X

Keyword(s): Anomaly Detection, Online Retail Sector, Key Performance Indicators, Time-series Prediction, Deep Learning.

Related Ontology Subjects/Areas/Topics: Applications ; Business Analytics ; Business Intelligence ; Change Detection ; Data Engineering ; Informatics in Control, Automation and Robotics ; Predictive Modeling ; Signal Processing, Sensors, Systems Modeling and Control ; Software Engineering

Abstract: As the e-commerce sales grow in global retail sector year by year, detecting anomalies that occur in the most important key performance indicators (KPI) in real-time has become a critical requirement for e-commerce companies. Such anomalies that may arise from software updates, server failures, or incorrect price entries cause substantial revenue loss in the meantime until they are detected with their root-causes. In this paper, we present a comparative analysis of various anomaly detection methods in detecting e-commerce anomalies. For this purpose, we first present the univariate analysis of six commonly used anomaly detection methods on two important KPIs of an e-commerce website. The highest F1 Scores and recall values on the test sets of both KPIs are obtained using Long-Short Term Memory (LSTM) network, showing that LSTM fits better to the dynamics of e-commerce KPIs than time-series based prediction methods. Then, in addition to the univariate analysis of the methods, we feed t he campaign information into LSTM network considering that campaigns have significant effects on the values of KPIs in e-commerce domain and this information can be helpful to prevent false positives that may occur in the campaign periods. The results also show that constructing a multivariate LSTM by feeding the campaign information as an additional input improves the adaptability of the model to sudden changes occurring in campaign periods. (More)

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Paper citation in several formats:
Bozbura, M.; Tunc, H.; Kusak, M. and Sakar, C. (2019). Detection of e-Commerce Anomalies using LSTM-recurrent Neural Networks.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, ISSN 2184-285X, pages 217-224. DOI: 10.5220/0007924502170224

author={Merih Bozbura. and Hunkar C. Tunc. and Miray Endican Kusak. and C. Okan Sakar.},
title={Detection of e-Commerce Anomalies using LSTM-recurrent Neural Networks},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},


JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Detection of e-Commerce Anomalies using LSTM-recurrent Neural Networks
SN - 978-989-758-377-3
AU - Bozbura, M.
AU - Tunc, H.
AU - Kusak, M.
AU - Sakar, C.
PY - 2019
SP - 217
EP - 224
DO - 10.5220/0007924502170224

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