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2012 IEEE 42nd International Symposium on Multiple-Valued Logic
SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model
Victoria, British Columbia Canada
May 14-May 16
ISBN: 978-0-7695-4673-5
| ASCII Text | x | ||
| Alejandro Veloz, Rodrigo Salas, Héctor Allende-Cid, Héctor Allende, "SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model," 2013 IEEE 43rd International Symposium on Multiple-Valued Logic, pp. 226-231, 2012 IEEE 42nd International Symposium on Multiple-Valued Logic, 2012. | |||
| BibTex | x | ||
| @article{ 10.1109/ISMVL.2012.42, author = {Alejandro Veloz and Rodrigo Salas and Héctor Allende-Cid and Héctor Allende}, title = {SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model}, journal ={2013 IEEE 43rd International Symposium on Multiple-Valued Logic}, volume = {0}, year = {2012}, issn = {0195-623X}, pages = {226-231}, doi = {http://doi.ieeecomputersociety.org/10.1109/ISMVL.2012.42}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2013 IEEE 43rd International Symposium on Multiple-Valued Logic TI - SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model SN - 0195-623X SP226 EP231 A1 - Alejandro Veloz, A1 - Rodrigo Salas, A1 - Héctor Allende-Cid, A1 - Héctor Allende, PY - 2012 KW - Lags identification KW - nonlinear autoregressive time series models KW - Takagi-Sugeno-Kang fuzzy model VL - 0 JA - 2013 IEEE 43rd International Symposium on Multiple-Valued Logic ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISMVL.2012.42
In this work, a Takagi-Sugeno-Kang (TSK) model is used for time series analysis and some important questions about the identification of this kind of models are addressed: the identification of the model structure and the set of the most influential regressors or lags. The main idea behind of the proposed method resembles to those techniques that prioritize lags evaluating the proximity of nearby samples in the input space in relation to the closeness of the corresponding target values. Clusters of samples are generated and the consistence of the mapping between the predicted variable and the set of candidate past values is evaluated. Afterwards, a TSK model is established and the redundancies in the rule base are avoided. Simulation experiments were conducted for 2 synthetic nonlinear autoregressive processes and for 4 benchmark time series. Results show a promising performance in terms of forecasting error and in terms of ability to find a proper set of lags of a given autoregressive process.
Index Terms:
Lags identification, nonlinear autoregressive time series models, Takagi-Sugeno-Kang fuzzy model
Citation:
Alejandro Veloz, Rodrigo Salas, Héctor Allende-Cid, Héctor Allende, "SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model," ismvl, pp.226-231, 2012 IEEE 42nd International Symposium on Multiple-Valued Logic, 2012
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