Abstract
This paper presents a procedure for parameter estimation of the neuro-coefficient smooth transition autoregressive model, substituting the combination of grid search and local search of the original proposal of Medeiros and Veiga (2005, IEEE Trans. NN, 16(1):97-113) with a differential evolution scheme. The purpose of this novel fitting procedure is to obtain more accurate models under preservation of the most important model characteristics. These are, firstly, that the models are built using an iterative approach based on statistical tests, and therewith have a mathematically sound construction procedure. And secondly, that the models are interpretable in terms of fuzzy rules. The proposed procedure has been tested empirically by applying it to different real-world time series. The results indicate that, in terms of accuracy, significantly improved models can be achieved, so that accuracy of the resulting models is comparable to other standard time series forecasting methods.
Original language | English |
---|---|
Title of host publication | Hybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings |
Publisher | Springer-Verlag London Ltd. |
Pages | 464-473 |
Number of pages | 10 |
Volume | 7208 LNAI |
Edition | PART 1 |
ISBN (Print) | 9783642289415 |
DOIs | |
Publication status | Published - 2012 |
Externally published | Yes |
Event | International Conference on Hybrid Artificial Intelligence Systems 2012 - Salamanca, Spain Duration: 28 Mar 2012 → 30 Mar 2012 Conference number: 7th |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Number | PART 1 |
Volume | 7208 LNAI |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | International Conference on Hybrid Artificial Intelligence Systems 2012 |
---|---|
Abbreviated title | HAIS 2012 |
Country/Territory | Spain |
City | Salamanca |
Period | 28/03/12 → 30/03/12 |
Keywords
- differential evolution
- NCSTAR
- statistical models
- threshold autoregressive models
- time series