Evolving ensemble of fuzzy models for multivariate time series prediction

Lourenco Bueno, Pyramo Costa, Israel Mendes, Enderson Cruz, Daniel Leite

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Weather modeling and prediction has been quite a challenge over the years. Predictions based on climatic models whose dynamical behavior is nonlinear, nonstationary, and based on high order difference equations is a tough task and usually requires a demanding and non-intuitive tuning expertise. This paper suggests an ensemble of evolving fuzzy models for multivariate time series prediction. The proposed ensemble approach is able to model the weather dynamics from data streams concerning variables such as wet bulb temperature, atmospheric pressure, maximum temperature, and relative humidity of the air. The purpose is to predict rainfalls 5 days ahead while providing a linguistic description of the reasoning used to give the predictions. Empirical results show that the ensemble-based fuzzy evolving modeling approach outperforms other evolving approaches in terms of accurate predictions.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
EditorsAdnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467374286
DOIs
StatePublished - 25 Nov 2015
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: 2 Aug 20155 Aug 2015

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2015-November
ISSN (Print)1098-7584

Conference

ConferenceIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Country/TerritoryTurkey
CityIstanbul
Period2/08/155/08/15

Keywords

  • Adaptation models
  • Atmospheric modeling
  • Data models
  • Fuzzy sets
  • Mathematical model
  • Meteorology
  • Predictive models

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