A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters

被引:0
|
作者
José Luis Aznarte
Daniel Molina
Ana M. Sánchez
José M. Benítez
机构
[1] UNED,Dept. of Artificial Intelligence
[2] University of Cádiz,Dept. Computer Languages and Systems
[3] University of Granada,Dept. Software Engineering
[4] Universidad de Granada,Dept. of Computational Sciences and A. I., CITIC
来源
Applied Intelligence | 2011年 / 34卷
关键词
Fuzzy rule-based systems; Heteroscedasticity; Residuals; Diagnostic checking;
D O I
暂无
中图分类号
学科分类号
摘要
Heteroscedasticy is the property of having a changing variance throughout the time. Homoscedasticity is the converse, that is, having a constant variance. This is a key property for time series models which may have serious consequences when making inferences out of the errors of a given forecaster. Thus it has to be conveniently assessed in order to establish the quality of the model and its forecasts. This is important for every model including fuzzy rule-based systems, which have been applied to time series analysis for many years. Lagrange multiplier testing framework is used to evaluate wether the residuals of an FRBS are homoscedastic. The test robustness is thoroughly evaluated through an extensive experimentation. This is another important step towards a statistically sound modeling strategy for fuzzy rule-based systems.
引用
收藏
页码:386 / 393
页数:7
相关论文
共 50 条
  • [21] Counterfactual rule generation for fuzzy rule-based classification systems
    Zhang, Te
    Wagner, Christian
    Garibaldi, Jonathan. M.
    2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2022,
  • [22] Fuzzy Rule-Based Classification Method for Incremental Rule Learning
    Niu, Jiaojiao
    Chen, Degang
    Li, Jinhai
    Wang, Hui
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (09) : 3748 - 3761
  • [23] Effect of rule weights in fuzzy rule-based classification systems
    Ishibuchi, H
    Nakashima, T
    NINTH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2000), VOLS 1 AND 2, 2000, : 59 - 64
  • [24] Adaptability, interpretability and rule weights in fuzzy rule-based systems
    Riid, Andri
    Ruestern, Ennu
    INFORMATION SCIENCES, 2014, 257 : 301 - 312
  • [25] Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems
    Henzgen, Sascha
    Strickert, Marc
    Huellermeier, Eyke
    PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013, 2013, 226 : 279 - 288
  • [26] Inconsistency resolution and rule insertion for fuzzy rule-based systems
    Lee, HM
    Chen, JM
    Liu, CL
    JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2002, 18 (02) : 187 - 210
  • [27] Rule-based joint fuzzy and probabilistic networks
    Yadegari, M.
    Seyedin, S. A.
    IRANIAN JOURNAL OF FUZZY SYSTEMS, 2020, 17 (03): : 135 - 149
  • [28] Designing Distributed Fuzzy Rule-Based Models
    Cui, Ye
    E, Hanyu
    Pedrycz, Witold
    Li, Zhiwu
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (07) : 2047 - 2053
  • [29] A framework for fuzzy rule-based cognitive maps
    Khan, MS
    Khor, SW
    PRICAI 2004: TRENDS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2004, 3157 : 454 - 463
  • [30] On the Potential of Fuzzy Rule-Based Ensemble Forecasting
    Sikora, David
    Stepnicka, Martin
    Vavrickova, Lenka
    INTERNATIONAL JOINT CONFERENCE CISIS'12 - ICEUTE'12 - SOCO'12 SPECIAL SESSIONS, 2013, 189 : 487 - +