Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)

TL;DR

This paper introduces SFCA, a novel method for predicting sleep and work hours from internet activity and electricity data, demonstrating superior performance over existing techniques across multiple datasets.

Contribution

The paper presents SFCA, a new segmented functional classification technique that improves prediction accuracy of human activity patterns from diverse alternative data sources.

Findings

SFCA outperforms existing methods in predicting sleep and work hours.

Effective across internet activity and electricity demand datasets.

Demonstrates the potential of alternative data for behavioral insights.

Abstract

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.