From Twitter to Swift: Constructing Anomaly Detection.
Twitter (now X), again in 2015 made an Anomaly Detection Algorithm for use in tracking trends among their millions of users.
This package deal, made completely in R, remains to be very usable. It was designed to be able to detect global and local anomalies, and it is ready to efficiently detect all kinds of anomalies. For an entire checklist of what it could and may’t detect please try Anomaly.io’s test of the original algorithm, as it is rather complete.
Why not 🤷♂️? I used to be bored.
Twitter’s Anomaly Detection Algorithm is a statistical framework designed for detecting anomalies, or outliers, in a time-series dataset.
There are two essential core parts to the algorithm.
- Seasonal Decomposition: The algorithm…