Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)
This provides a novel method for analyzing human behavior patterns at a global scale, though it is incremental in applying functional classification to new types of data.
The paper tackled the problem of predicting sleep and work hours from alternative data, such as internet activity and electricity demand, by developing Segmented Functional Classification Analysis (SFCA), which outperformed existing methods across both datasets.
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.