Real-time Anomaly Detection in Epidemic Data Streams

Pulak Agarwal, Pranav Aluru, B. Aditya Prakash

Keywords: Machine learning, time-series data, real-time anomaly detection

TL;DR: We present a classifier that will accurately detect real-time anomalies in COVID-19 reporting time-series.

Abstract: A key challenge with data collection for time-series data for forecasting is detecting anomalies in real-time. Anomalies can negatively affect the performance of forecasting models, if not accounted for. It is a relatively simple process to retrospectively identify an anomaly in a time-series and correct it, however the problem lies with identifying a particular time-step as anomalous when there is no further information to distinguish it from a change in trend. In the context of the COVID-19 pandemic, accurate forecasts are critical to policymakers, and yet, most data is recorded manually, susceptible to human errors or other technical constraints. While most errors are corrected, it often takes several weeks for these corrections to be made, leading to real-time forecasting models generating predictions on data that is not necessarily accurate. In order to prevent this, we utilize a neural framework called Back2Future and build a simple classifier on top of it to propose a real-time anomaly detection system in the context of detecting anomalies in COVID-19 reporting data.