R-Checkpoint algorithm for multi-event decision making over multivariate time series

Chun Kit Ngan, Alexander Brodsky, Jessica Lin

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

Abstract

We propose a Relaxed Checkpoint algorithm (R-Checkpoint) to solve Multi-Event Expert Query Parametric Estimation (ME-EQPE) problems over multivariate time series. Our proposed algorithm combines the strengths of both domain-knowledge-based and formal-learning-based approaches to learn decision parameters for yielding a reasonable time utility over multivariate time series. More specifically, our approach solves the decision optimization problems to yield the time utility from multiple decision time points, as well as learns the multiple sets of decision parameters in their respective events during the computations at a lower cost. We show that our approach produces a reasonable forecasting result by using the learned multiple sets of decision parameters.

Original languageEnglish (US)
Title of host publicationFusing Decision Support Systems into the Fabric of the Context
PublisherIOS Press
Pages209-220
Number of pages12
ISBN (Print)9781614990727
DOIs
StatePublished - 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume238
ISSN (Print)0922-6389

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Ngan, C. K., Brodsky, A., & Lin, J. (2012). R-Checkpoint algorithm for multi-event decision making over multivariate time series. In Fusing Decision Support Systems into the Fabric of the Context (pp. 209-220). (Frontiers in Artificial Intelligence and Applications; Vol. 238). IOS Press. https://doi.org/10.3233/978-1-61499-073-4-209