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Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks

Authors:

Chin-Chia Michael Yeh,

Zhongfang Zhuang,

Javid Ebrahimi,

Wei ZhangAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4331 - 4340

https://doi.org/10.1145/3459637.3481942

Published: 30 October 2021 Publication History

Abstract

Predicting metrics associated with entities' transnational behavior within payment processing networks is essential for system monitoring. Multivariate time series, aggregated from the past transaction history, can provide valuable insights for such prediction. The general multivariate time series prediction problem has been well studied and applied across several domains, including manufacturing, medical, and entomology. However, new domain-related challenges associated with the data such as concept drift and multi-modality have surfaced in addition to the real-time requirements of handling the payment transaction data at scale. In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database. We propose a model with five unique components to estimate the transaction metrics from multi-modality data. Four of these components capture interaction, temporal, scale, and shape perspectives, and the fifth component fuses these perspectives together. We also propose a hybrid offline/online training scheme to address concept drift in the data and fulfill the real-time requirements. Combining the estimation model with a graphical user interface, the prototype transaction metric estimation system has demonstrated its potential benefit as a tool for improving a payment processing company's system monitoring capability.

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Cited By

Yeh CDai XZheng YWang JChen HFan YDer AZhuang ZWang LZhang W(2023)Multitask Learning for Time Series Data with 2D Convolution2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00010(9-16)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00010
Yeh CZheng YPan MChen HZhuang ZWang JWang LZhang WPhillips JKeogh E(2023)Sketching Multidimensional Time Series for Fast Discord Mining2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386363(443-452)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386363
Wu HElizaveta DZhadan APetrosian O(2023)Forecasting online adaptation methods for energy domainEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106499123:PCOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106499

Index Terms

Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Online learning settings
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Yeh CDai XZheng YWang JChen HFan YDer AZhuang ZWang LZhang W(2023)Multitask Learning for Time Series Data with 2D Convolution2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00010(9-16)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00010
Yeh CZheng YPan MChen HZhuang ZWang JWang LZhang WPhillips JKeogh E(2023)Sketching Multidimensional Time Series for Fast Discord Mining2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386363(443-452)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386363
Wu HElizaveta DZhadan APetrosian O(2023)Forecasting online adaptation methods for energy domainEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106499123:PCOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106499

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