Authors:
Reginald Bolman
and
Thomas Boucher
Affiliation:
Department of Mathematics, Texas A&M University-Commerce, 2200 Campbell St, Commerce TX and U.S.A.
Keyword(s):
Morlet Wavelets, Financial Time Series Datasets, Datamining, Financial Time Series Analysis, Wavelet Analysis, Time Series Power Comparison.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Statistics Exploratory Data Analysis
;
Symbolic Systems
Abstract:
Wavelets are a family of signal processing techniques which have a growing popularity in the artificial intelligence community. In particular, Morlet wavelets have been applied to neural network time series trend prediction, forecasting the effects of monetary policy, etc. In this paper, we discuss the application of Morlet wavelets to discover the morphology of a time series cyclical components and the unsupervised data mining of financial time series in order to discover hidden motifs within the data. To perform the analysis of a given time series and form a comparison between the morphologies this paper proposes the implementation of the “Bolman Time Series Power Comparison” algorithm which will extract the pertinent time series motifs from the underlying dataset.