Abstract
In time series analysis remaining autocorrelation in the errors of a model implies that it is failing to properly capture the structure of time-dependence of the series under study. This can be used as a diagnostic checking tool and as an indicator of the adequacy of the model. Through the study of the errors of the model in the Lagrange Multiplier testing framework, in this paper we derive (and validate using simulated and real world examples) a hypothesis test which allows us to determine if there is some left autocorrelation in the error series. This represents a new diagnostic checking tool for fuzzy rule-based modelling of time series and is an important step towards statistically sound modelling strategy for fuzzy rule-based models.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 371-387 |
| Number of pages | 17 |
| Journal | International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems |
| Volume | 18 |
| Issue number | 4 |
| DOIs | |
| State | Published - Aug 2010 |
| Externally published | Yes |
Keywords
- autocorrelation
- fuzzy rule based models
- residual analysis
- Statistical test
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Information Systems
- Artificial Intelligence