Algorithms For Anomaly Detection on Time Series: A Use Case on Banking Data
Abstract
The present research aims to present an overview of methods for automatically detecting anomalies in data representing time series. A time series is a sequence of qualitative values obtained at successive times, generally measured with equal intervals. Time series can represent different real-life phenomena, such as the behaviour of the stock market, variations in temperature and other meteorological data, the behaviour of banking credit/debit card consumption, among others. In addition, this work presents a 4-step methodology for preprocessing data and detecting anomalies on a time series dataset representing the spending of debit and credit card customers. A synthetic anomaly injection technique was applied to validate the models. Results can be used to monitor banking behaviour and trigger alarms in case of possible fraud or rare events.References
Anomaly detection
data mining
banking data
DOI:
https://doi.org/10.31449/inf.v49i13.6243Downloads
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