Building explainable predictive analytics for location-dependent time-series data

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 environmental phenomena. The challenges include how to identify the main features driving the phenomena, how to handle complex spatiotemporal variations in the phenomena, and how to utilize sparse ground truth measurements for training and validation. The resulting framework will enable fine spatial and temporal scale environmental exposure assessment and allow researchers to carry out unprecedented inquiries, such as understanding relationships between health outcomes and long-term air pollution exposures.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 1st International Conference on Cognitive Machine Intelligence, CogMI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-209
Number of pages8
ISBN (Electronic)9781728167374
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event1st IEEE International Conference on Cognitive Machine Intelligence, CogMI 2019 - Los Angeles, United States
Duration: Dec 12 2019Dec 14 2019

Publication series

NameProceedings - 2019 IEEE 1st International Conference on Cognitive Machine Intelligence, CogMI 2019

Conference

Conference1st IEEE International Conference on Cognitive Machine Intelligence, CogMI 2019
Country/TerritoryUnited States
CityLos Angeles
Period12/12/1912/14/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Machine learning
  • Predictive analytics
  • Spatial data science
  • Spatio temporal data

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