Fuzzy rule-based ensemble with use linguistic associations mining for time series prediction
Lenka Št pni ková, Martin Stepnicka, David Sikora
Lenka Št pni ková
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.63How to use a DOI?
- Time series fuzzy rules ensembles Fuzzy Rule Based Ensemble fuzzy GUHA linguistic associations perception-based logical deduction
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/eusflat.2013.63 ID - Štpniková2013/08 ER -