State space modeling of time series
State space modeling of time series
Information Sciences: an International Journal
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
Parallel implementation of a VARMAX algorithm
Parallel Computing
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Evolutionary algorithms for constrained engineering problems
Computers and Industrial Engineering
Representation and application of fuzzy numbers
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Some remarks on distances between fuzzy numbers
Fuzzy Sets and Systems
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Multi-objective fuzzy regression: a general framework
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Outliers detection and confidence interval modification in fuzzy regression
Fuzzy Sets and Systems
Multiobjective fuzzy regression with central tendency and possibilistic properties
Fuzzy Sets and Systems
Geno-mathematical identification of the multi-layer perceptron
Neural Computing and Applications
Data mining for data classification based on the KNN-fuzzy method supported by genetic algorithm
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
A theoretical development on a fuzzy distance measure for fuzzy numbers
Mathematical and Computer Modelling: An International Journal
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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The K nearest neighbors approach is a viable technique in time series analysis when dealing with ill-conditioned and possibly chaotic processes. Such problems are frequently encountered in, e.g., finance and production economics. More often than not, the observed processes are distorted by nonnormal disturbances, incomplete measurements, etc. that undermine the identification, estimation and performance of multivariate techniques. If outliers can be duly recognized, many crisp statistical techniques may perform adequately as such. Geno-mathematical programming provides a connection between statistical time series theory and fuzzy regression models that may be utilized e.g., in the detection of outliers. In this paper we propose a fuzzy distance measure for detecting outliers via geno-mathematical parametrization. Fuzzy KNN is connected as a linkable library to the genetic hybrid algorithm (GHA) of the author, in order to facilitate the determination of the LR-type fuzzy number for automatic outlier detection in time series data. We demonstrate that GHA[Fuzzy KNN] provides a platform for automatically detecting outliers in both simulated and real world data.