Mining for similarities in time series data using wavelet-based feature vectors and neural networks

Authors:
P. K. Dash;Maya Nayak;M. R. Senapati;I. W. C. Lee
Affiliations:
College of Engineering, Bhubaneswar, India;College of Engineering, Bhubaneswar, India;College of Engineering, Bhubaneswar, India;University of Calgary, Canada
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 3
Cited 9

Adaptive filter theory (2nd ed.)

Adaptive filter theory (2nd ed.)
Localization of the complex spectrum: the S transform

IEEE Transactions on Signal Processing
Quality enhances reliability [power supplies]

IEEE Spectrum

Non-stationary power signal processing for pattern recognition using HS-transform

Applied Soft Computing
Power signal classification using dynamic wavelet network

Applied Soft Computing
A review on time series data mining

Engineering Applications of Artificial Intelligence
A hybrid ant colony optimization technique for power signal pattern classification

Expert Systems with Applications: An International Journal
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line

Computers and Industrial Engineering
Feature selection for classification of oscillating time series

Expert Systems: The Journal of Knowledge Engineering
Power quality event characterization using support vector machine and optimization using advanced immune algorithm

Neurocomputing
Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations

Expert Systems with Applications: An International Journal
Time frequency analysis and power signal disturbance classification using support vector machine and differential evolution algorithm

International Journal of Knowledge-based and Intelligent Engineering Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a comparison between different wavelet feature vectors for data mining of nonstationary time series that occurs in an electricity supply network. Three different wavelet algorithms are simulated and applied on nine classes of power signal time series, which primarily belongs to an important problem area called electric power quality. In contrast to the wavelet analysis, the paper presents a new approach called S-transform-based time frequency analysis in processing power quality disturbance data. Certain pertinent feature vectors are extracted using the well-known wavelet methods and the new approach using S-transform. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results. Further in large data sets partitioning is done and similarities of pattern vectors present in different sections are determined. The approach is a general one and can be applied to pattern classification, similarity determination, and knowledge discovery in time varying data patterns occurring in many practical sciences and engineering problems.