  Alternative data is increasingly adapted to predict human and economic
behaviour. This paper introduces a new type of alternative data by
re-conceptualising the internet as a data-driven insights platform at global
scale. Using data from a unique internet activity and location dataset drawn
from over 1.5 trillion observations of end-user internet connections, we
construct a functional dataset covering over 1,600 cities during a 7 year
period with temporal resolution of just 15min. To predict accurate temporal
patterns of sleep and work activity from this data-set, we develop a new
technique, Segmented Functional Classification Analysis (SFCA), and compare its
performance to a wide array of linear, functional, and classification methods.
To confirm the wider applicability of SFCA, in a second application we predict
sleep and work activity using SFCA from US city-wide electricity demand
functional data. Across both problems, SFCA is shown to out-perform current
methods.
