SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model
- Resource Type
- Conference
- Authors
- Veloz, Alejandro; Salas, Rodrigo; Allende-Cid, Héctor; Allende, Héctor
- Source
- 2012 IEEE 42nd International Symposium on Multiple-Valued Logic Multiple-Valued Logic (ISMVL), 2012 42nd IEEE International Symposium on. :226-231 May, 2012
- Subject
- Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Time series analysis
Mathematical model
Fuzzy sets
Training
Benchmark testing
Predictive models
Lags identification
nonlinear autoregressive time series models
Takagi-Sugeno-Kang fuzzy model
- Language
- ISSN
- 0195-623X
2378-2226
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.