Abstract

There are increasing numbers of online sources of real-time and historical location-dependent time-series data describing various types of environmental phenomena, e.g., traffic conditions and air quality levels. When coupled with the information that characterizes the natural and built environments, these location-dependent time-series data can help better understand interactions between and within human social systems and the ecosystem. Nevertheless, these data are still limited by their spatial and temporal resolution for downstream use (e.g., generating residential-level environmental exposures for human health studies). In this paper, we present a vision of a general machine learning framework for explainable predictive analytics for location-dependent time-series data. The framework will effectively deal with data-and model-related challenges for general scientific predictive analytics on spatiotemporal …

Metadata

publication

2019 IEEE First International Conference on Cognitive Machine Intelligence …, 2019

year

2019

publication date

2019/12/12

authors

Yao-Yi Chiang, Yijun Lin, Meredith Franklin, Sandrah P Eckel, José Luis Ambite, Wei-Shinn Ku

link

https://ieeexplore.ieee.org/abstract/document/8998977/

resource_link

https://yaoyichi.github.io/papers/Chiang-et-al.-2019-Building-Explainable-Predictive-Analytics-for-Location-Dependent-Time-Series-Data.pdf

conference

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

pages

202-209

publisher

IEEE