论文标题
通过分段功能分类分析（SFCA）从替代数据估算睡眠和工作时间
Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)
论文作者
论文摘要
替代数据越来越适合预测人类和经济行为。本文通过将Internet作为全球范围的数据驱动的Insights平台重新概念化，介绍了一种新型的替代数据。使用来自独特的Internet活动和位置数据集的数据，这些数据来自最终用户Internet连接的1.5万亿观察结果，我们构建了一个功能性数据集，该数据集在7年期间覆盖了1,600多个城市，而时间分辨率仅为15分钟。为了预测此数据集的准确时间模式和工作活动，我们开发了一种新技术，分割的功能分类分析（SFCA），并将其性能与各种线性，功能和分类方法进行比较。为了确认SFCA的更广泛适用性，在第二个应用程序中，我们使用美国全市范围的电力需求功能数据预测睡眠和工作活动。在这两个问题中，SFCA均显示出均超过当前方法。
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.
