Non-uniform attractor embedding for time series forecasting by fuzzy inference systems

Authors:
Minvydas Ragulskis;Kristina Lukoseviciute
Affiliations:
Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas, LT-51638, Lithuania;Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas, LT-51638, Lithuania
Venue:
Neurocomputing
Year:
2009

Citing 7
Cited 8

Practical method for determining the minimum embedding dimension of a scalar time series

Physica D
Nonlinear time series analysis

Nonlinear time series analysis
Neural Network Time Series Forecasting of Financial Markets

Neural Network Time Series Forecasting of Financial Markets
A novel approach for estimation of optimal embedding parameters of nonlinear time series by structural learning of neural network

Neurocomputing
Using adaptive neuro-fuzzy inference system for hydrological time series prediction

Applied Soft Computing
A bivariate fuzzy time series model to forecast the TAIEX

Expert Systems with Applications: An International Journal
A new hybrid artificial neural networks and fuzzy regression model for time series forecasting

Fuzzy Sets and Systems

Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems

Neurocomputing
Ant colony optimization for determining the optimal dimension and delays in phase space reconstruction

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

Neurocomputing
A fuzzy intelligent approach to the classification problem in gene expression data analysis

Knowledge-Based Systems
Plenary lecture 1: time series prediction based on fuzzy and neural networks

MMES'11/DEEE'11/COMATIA'11 Proceedings of the 2nd international conference on Mathematical Models for Engineering Science, and proceedings of the 2nd international conference on Development, Energy, Environment, Economics, and proceedings of the 2nd international conference on Communication and Management in Technological Innovation and Academic Globalization
A HMM-based adaptive fuzzy inference system for stock market forecasting

Neurocomputing
Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

Neurocomputing
Non uniform Embedding based on Relevance Analysis with reduced computational complexity: Application to the detection of pathologies from biosignal recordings

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

A new method for identification of an optimal set of time lags based on non-uniform attractor embedding from the observed non-linear time series is proposed in this paper. Simple deterministic method for the determination of non-uniform time lags comprises the pre-processing stage of the time series forecasting algorithm which is implemented in the form of a fuzzy inference system. Identification of embedding parameters of the underlying dynamical system includes not only optimization of time lags but also determination of optimal dimension of the reconstructed phase space. Experiments done with benchmark chaotic time series show that the proposed method can considerably improve the forecasting accuracy. The proposed method seems to be an efficient candidate for prediction of time series with multiple time scales and noise.