Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F15%3AA1501B27" target="_blank" >RIV/61988987:17610/15:A1501B27 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining
Original language description
As there are many various methods for time series prediction developed but none of them generally outperforms all the others, 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, that combine more individual forecasts, are being proposed. In this contribution, we employ the so called fuzzy rule-based ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases 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.
Czech name
—
Czech description
—

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Strengthening Links between Data Analysis and Soft Computing
ISBN
978-3-319-10764-6
ISSN
2194-5357
e-ISSN
—
Number of pages
11
Pages from-to
261-271
Publisher name
Springer
Place of publication
Heidelberg
Event location
Warsaw
Event date
Jan 1, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)