Online truth discovery on time series data

Liuyi Yao, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao, Aidong Zhang

Research output: Contribution to conferencePaperpeer-review

17 Scopus citations


Truth discovery, with the goal of inferring true information from massive data through aggregating the information from multiple data sources, has attracted significant attention in recent years. It has demonstrated great advantages in real applications since it can automatically learn the reliability degrees of the data sources without supervision and in turn helps to find more reliable information. In many applications, however, the data may arrive in a stream and present various temporal patterns. Unfortunately, there is no existing truth discovery work that can handle such time series data. To tackle this challenge, we propose a novel online truth discovery framework that incorporates the predictions on the time series data into the truth estimation process. By jointly considering the multi-source information and the temporal patterns of the time series data, the proposed framework can improve the accuracy of the truth discovery results as well as the time series prediction. The effectiveness of the proposed framework is validated on both synthetic and realworld datasets.

Original languageEnglish (US)
Number of pages9
StatePublished - 2018
Event2018 SIAM International Conference on Data Mining, SDM 2018 - San Diego, United States
Duration: May 3 2018May 5 2018


Conference2018 SIAM International Conference on Data Mining, SDM 2018
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software


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