@inproceedings{7a3ee83a868e46e7ad61017878f334de,
title = "Evolving ensemble of fuzzy models for multivariate time series prediction",
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.",
keywords = "Adaptation models, Atmospheric modeling, Data models, Fuzzy sets, Mathematical model, Meteorology, Predictive models",
author = "Lourenco Bueno and Pyramo Costa and Israel Mendes and Enderson Cruz and Daniel Leite",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 ; Conference date: 02-08-2015 Through 05-08-2015",
year = "2015",
month = nov,
day = "25",
doi = "10.1109/FUZZ-IEEE.2015.7338002",
language = "English",
series = "IEEE International Conference on Fuzzy Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Adnan Yazici and Pal, {Nikhil R.} and Hisao Ishibuchi and Bulent Tutmez and Chin-Teng Lin and Sousa, {Joao M. C.} and Uzay Kaymak and Trevor Martin",
booktitle = "FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems",
}