Surprises in time series analysis
Part of Time Series Connect webinar series
Monday 26 October 2026, 6:00 PM Eastern Time
Abstract
I will present a statistical framework for identifying anomalies in three time series settings: unusual observations within a single historical series, unusual series within a large collection, and real-time surveillance of incoming data streams.
In each case, anomalies are identified using a probabilistic approach based on ‘surprisal values’ — equal to minus the log (conditional) density of each observation — with extreme value theory used to model the tail of the surprisal distribution and avoid strong parametric assumptions.
The methods will be illustrated using the {weird} package for R, with examples including French and US mortality rates, and pharmaceutical sales.
Software
