Synthesis of Statistical Knowledge from Time-Dependent Data

David K.Y. Chiu, Andrew K.C. Wong, Chun Chung Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper presents a general approach to analyzing multivariate time-dependent system processes with discrete-valued (both nominal and ordinal) and/or continuous-valued outcomes. The approach is based on an event-covering approach which selects (or covers) a subspace from the outcome space of an n-tuple of variables for estimation purposes. From the covered subspace, statistically interdependent events are selected as statistical knowledge for forecasting unknown events. The event-covering method presented here is based on the use of restricted variables with only a subset of the outcomes considered. Extension to event-covering method based on the selection of joint outcomes is also discussed. We have tested this method using climatic data and simulated data which model situations in real life. The experiments successfully demonstrate both its flexibility and efficacy.
Original languageEnglish
Pages (from-to)265-271
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Jan 1991
Externally publishedYes

Keywords

  • Discrete-valued multivariate time series
  • event-covering
  • forecasting
  • inductive prediction
  • machine learning
  • mixed-mode multivariate time series

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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