Fuzzy rule-based ensemble with use linguistic associations mining for time series prediction

DOI

10.2991/eusflat.2013.63How to use a DOI?

Keywords

Time series fuzzy rules ensembles Fuzzy Rule Based Ensemble fuzzy GUHA linguistic associations perception-based logical deduction

Abstract

There are many various methods to forecast time series. However, there is no single forecasting method that generally outperforms any other. Consequently, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques are being proposed. These techniques combine more individual forecasting methods. In this contribution, we employ the so called fuzzy rule-based ensemble to determine the weights based on time series features such as trend, seasonality or stationarity. For identification of fuzzy rule base, we use linguistic association mining. An exhaustive experimental justification is provided.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lenka Št pni ková
AU  - Martin Stepnicka
AU  - David Sikora
PY  - 2013/08
DA  - 2013/08
TI  - Fuzzy rule-based ensemble with use linguistic associations mining for time series prediction
BT  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 448
EP  - 455
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.63
DO  - 10.2991/eusflat.2013.63
ID  - Štpniková2013/08
ER  -