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

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

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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  • Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020. "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers 2010.08102, arXiv.org.
    • Handle: RePEc:arx:papers:2010.08102
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    References listed on IDEAS

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