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

Citation Type: Working Paper

Publication Year: 2020

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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.

Url: https://arxiv.org/abs/2010.08102

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Authors: Ackerman, Klaus; Angus, Simon D.; Raschky, Paul A.

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Data Collections: IPUMS Time Use - ATUS

Topics: Work, Family, and Time

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