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

arXiv: https://arxiv.org/abs/2109.10020 · HTML: https://mlsanity.com/paper/2109.10020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [Merchant Category Identification Using Credit Card Transactions](https://mlsanity.com/paper/2011.02602/) (2020)
- [Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile](https://mlsanity.com/paper/1811.03064/) (2018)
- [Towards a Flexible Embedding Learning Framework](https://mlsanity.com/paper/2009.10989/) (2020)
- [Multi-future Merchant Transaction Prediction](https://mlsanity.com/paper/2007.05303/) (2020)
- [Multi-stream RNN for Merchant Transaction Prediction](https://mlsanity.com/paper/2008.01670/) (2020)

## Built on by (most cited first, 2 of 2)

- [Sketching Multidimensional Time Series for Fast Discord Mining](https://mlsanity.com/paper/2311.03393/) (2023)
- [Multitask Learning for Time Series Data with 2D Convolution](https://mlsanity.com/paper/2310.03925/) (2023)

## Abstract (arXiv)

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.