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arXiv · 2010.08102

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

Klaus Ackermann·Simon D. Angus·Paul A. Raschky
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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.

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BibTeXRIS

Klaus Ackermann, Simon D. Angus, Paul A. Raschky. 2020-10-16. Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA). https://arxiv.org/abs/2010.08102

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