| Title: |
Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA) |
| Authors: |
Ackermann, Klaus, Angus, Simon D., Raschky, Paul A. |
| Publication Year: |
2020 |
| Collection: |
Computer Science
Statistics |
| Subject Terms: |
Statistics - Applications, Computer Science - Machine Learning, Economics - Econometrics, Statistics - Methodology |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2010.08102 |
| Accession Number: |
edsarx.2010.08102 |
| Database: |
arXiv |
