Detecting ‘Lifestates’ From Digital Footprint Time Series Data

Introduction & BackgroundAs digital footprints data continue to grow in complexity and volume, understanding and summarising large, high-dimensional time series is becoming increasingly important for analysing behavioural patterns. In various domains, mass data collection is routine and crucial to detecting breakpoints (significant statistical changes) and underlying patterns. Examples of such data include: transactional records; internet history records; social media data. And yet, despite this explosion of data, it remains challenging to uncover commonalities across real-world time series that are both high-dimensional and noisy. Objectives & ApproachWe introduce a new method for deciphering complex digital footprints data named ALI (Automatic Lifestate Identification), a parameter-free algorithm which aims to create interpretable summaries of the data. ALI efficiently identifies breakpoints, and clusters resulting segments, referred to as lifestates, across multiple time series. ALI uses an Expectation Maximisation (EM) algorithm that iteratively searches for breakpoints and lifestates, meaning that each is informed by the other and improved on each iteration. The resul