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Authors: Reginald Bolman and Thomas Boucher

Affiliation: Department of Mathematics, Texas A&M University-Commerce, 2200 Campbell St, Commerce TX and U.S.A.

ISBN: 978-989-758-377-3

Keyword(s): Morlet Wavelets, Financial Time Series Datasets, Datamining, Financial Time Series Analysis, Wavelet Analysis, Time Series Power Comparison.

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.

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Paper citation in several formats:
Bolman, R. and Boucher, T. (2019). Data Mining using Morlet Wavelets for Financial Time Series.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 74-83. DOI: 10.5220/0007922200740083

@conference{data19,
author={Reginald Bolman. and Thomas Boucher.},
title={Data Mining using Morlet Wavelets for Financial Time Series},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={74-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007922200740083},
isbn={978-989-758-377-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Data Mining using Morlet Wavelets for Financial Time Series
SN - 978-989-758-377-3
AU - Bolman, R.
AU - Boucher, T.
PY - 2019
SP - 74
EP - 83
DO - 10.5220/0007922200740083

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