Erratum to “A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction” [Neurocomputing, 81 (2012) (1–11)]

AbstractSimplifying fuzzy models, including those for predicting time series, is an important issue in terms of their interpretation and implementation. This simplification can involve both the number of inference rules (i.e., structure) and the number of parameters. This paper proposes novel hybrid methods for time series prediction that utilize Takagi–Sugeno fuzzy systems with reduced structure. The fuzzy sets are obtained using a global optimization algorithm (particle swarm optimization, simulated annealing, genetic algorithm, or pattern search). The polynomials are determined by elastic net regression, which is a sparse regression. The simplification is based on reducing the number of polynomial parameters in the then-part by using sparse regression and removing unnecessary rules by using labels. A new quality criterion is proposed to express a compromise between the model accuracy and its simplification. The experimental results show that the proposed methods can improve a fuzzy model while simplifying its structure.

Download Full-text

A novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data

2007 IEEE Congress on Evolutionary Computation ◽

10.1109/cec.2007.4424485 ◽

2007 ◽

Cited By ~ 3

Author(s):

Ashish Anand ◽

P.N. Suganthan ◽

Kalyanmoy Deb

Keyword(s):

Time Series ◽

Evolutionary Algorithm ◽

Expression Data ◽

Short Time Series ◽

Multiobjective Evolutionary Algorithm ◽

Gene Assignment ◽

Series Expression ◽

Short Time

Download Full-text

Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2012.2227148 ◽

2013 ◽

Vol 24 (2) ◽

pp. 207-218 ◽

Cited By ~ 82

Author(s):

A. Miranian ◽

M. Abdollahzade

Keyword(s):

Time Series ◽

Support Vector Machines ◽

Least Squares ◽

Time Series Prediction ◽

Fuzzy Model ◽

Chaotic Time Series ◽

Support Vector ◽

Neuro Fuzzy ◽

Vector Machines ◽

Chaotic Time Series Prediction

Download Full-text

Fuzzy Supervised Multi-Period Time Series Forecasting

Cybernetics and Information Technologies ◽

10.2478/cait-2019-0016 ◽

2019 ◽

Vol 19 (2) ◽

pp. 74-86

Author(s):

Galina Ilieva

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Fuzzy Model ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Training Phase ◽

Fuzzy Input ◽

Multidimensional Matrix ◽

Historical Periods ◽

Better Than

Abstract The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies is constructed. During the test stage, the fitted fuzzy model is run for validating the observations and each output value is predicted by using a fuzzy input vector of k previous intervals. The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods. Comparative analysis shows the high potential of the new algorithm as an alternative to fuzzy prediction and reveals some opportunities for its further improvement.

Download Full-text

A Fuzzy Time Series Prediction Method Using the Evolutionary Algorithm

Lecture Notes in Computer Science - Advances in Intelligent Computing ◽

10.1007/11538356_55 ◽

2005 ◽

pp. 530-537 ◽

Cited By ~ 6

Author(s):

Hwan Il Kang

Keyword(s):

Time Series ◽

Evolutionary Algorithm ◽

Time Series Prediction ◽

Prediction Method ◽

Fuzzy Time Series

Download Full-text

HMM based Fuzzy Model for Time Series Prediction

2006 IEEE International Conference on Fuzzy Systems ◽

10.1109/fuzzy.2006.1681994 ◽

2006 ◽

Cited By ~ 7

Author(s):

R. Hassan ◽

B. Nath ◽

M. Kirley

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Fuzzy Model

Download Full-text

A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction

Data ◽

10.3390/data3040062 ◽

2018 ◽

Vol 3 (4) ◽

pp. 62 ◽

Cited By ~ 4

Author(s):

Alexander Vlasenko ◽

Nataliia Vlasenko ◽

Olena Vynokurova ◽

Dmytro Peleshko

Keyword(s):

Time Series ◽

Learning Algorithm ◽

Time Series Prediction ◽

Multivariate Time Series ◽

Fuzzy Model ◽

Nonlinear Behavior ◽

Additional Information ◽

Computational Performance ◽

Neuro Fuzzy ◽

Competing Models

Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

Download Full-text