Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA

Klaus Ackermann (), Simon Angus () and Paul Raschky
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Klaus Ackermann: SoDa Laboratories, Monash University

No 2020-04, SoDa Laboratories Working Paper Series from Monash University, SoDa Laboratories

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 ac- curate 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.

Keywords: functional data analysis; time use; electricity demand; big data; alternative data (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 J22 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-big and nep-lma
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