Testing for Serial Independence of the Residuals in the Framework of Fuzzy Rule-Based Time Series Modeling

2009 ◽  
Author(s):  
José Luis Aznarte M. ◽  
Antonio Arauzo ◽  
José Manuel Benítez Sánchez
Keyword(s):  
Time Series ◽  
Fuzzy Rule ◽  
Time Series Modeling ◽  
Rule Based ◽  
Serial Independence

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.

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

scholarly journals Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model

2021 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
O. Burak Akgun ◽  
Elcin Kentel
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Regional Climate ◽  
Fuzzy Rule ◽  
Regional Climate Models ◽  
Subtractive Clustering ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rule Based

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

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