Traffic Flow Predicting of Chaos Time Series Using Support Vector Learning Mechanism for Fuzzy Rule-based Modeling

2007 ◽  
Author(s):  
Pang Ming-bao ◽  
He Guo-guang
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Fuzzy Rule ◽  
Support Vector ◽  
Rule Based ◽  
Learning Mechanism

scholarly journals Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling

2005 ◽  
Vol 54 (7) ◽  
pp. 3009
Author(s):  
Cui Wan-Zhao ◽  
Zhu Chang-Chun ◽  
Bao Wen-Xing ◽  
Liu Jun-Hua
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Fuzzy Rule ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Rule Based ◽  
Vector Machines

TESTING FOR REMAINING AUTOCORRELATION OF THE RESIDUALS IN THE FRAMEWORK OF FUZZY RULE-BASED TIME SERIES MODELLING

2010 ◽  
Vol 18 (04) ◽  
pp. 371-387 ◽  
Author(s):  
JOSÉ LUIS AZNARTE ◽  
MARCELO C. MEDEIROS ◽  
JOSÉ M. BENÍTEZ
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Dependence ◽  
Real World ◽  
Hypothesis Test ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Testing Framework ◽  
Diagnostic Checking ◽  
Time Series Modelling

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.

Sign in / Sign up

Export Citation Format

Share Document