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Authors: Jian Cheng Wong 1 ; Gladys Hui Ting Lee 1 ; Yiting Zhang 1 ; Woei Shyr Yim 1 ; Robert Paulo Fornia 2 ; Danny Yuan Xu 3 ; Jun Liang Kok 1 and Siew Ann Cheong 1

Affiliations: 1 Nanyang Technological University, Singapore ; 2 University of Colorado at Boulder, United States ; 3 Bard College, United States

ISBN: 978-989-8425-79-9

Keyword(s): Time series segmentation, Coarse graining, Macroeconomic cycle, Financial markets.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining High-Dimensional Data ; Symbolic Systems ; Visual Data Mining and Data Visualization

Abstract: In this paper we explain how the dynamics of a complex system can be understood in terms of the lowdimensional manifolds (phases), described by slowly varying effective variables, it settles onto. We then explain how we can discover these phases by grouping the large number of microscopic time series or time series segments, based on their statistical similarities, into the a small number of time series classes, each representing a distinct phase. We describe a specific recursive scheme for time series segmentation based on the Jensen-Shannon divergence, and check its performance against artificial time series data. We then apply the method on the high-frequency time series data of various US and Japanese financial market indices, where we found that the time series segments can be very naturally grouped into four to six classes, corresponding roughly with economic growth, economic crisis, market correction, and market crash. From a single time series, we can estimate the lifetimes of these macroeconomic phases, and also identify potential triggers for each phase transition. From a cross section of time series, we can further estimate the transition times, and also arrive at an unbiased and detailed picture of how financial markets react to internal or external stimuli. (More)

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Paper citation in several formats:
Cheng Wong, J.; Hui Ting Lee, G.; Zhang, Y.; Shyr Yim, W.; Paulo Fornia, R.; Yuan Xu, D.; Liang Kok, J. and Ann Cheong, S. (2011). TIME SERIES SEGMENTATION AS A DISCOVERY TOOL - A Case Study of the US and Japanese Financial Markets.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 52-63. DOI: 10.5220/0003653700520063

@conference{kdir11,
author={Jian Cheng Wong. and Gladys Hui Ting Lee. and Yiting Zhang. and Woei Shyr Yim. and Robert Paulo Fornia. and Danny Yuan Xu. and Jun Liang Kok. and Siew Ann Cheong.},
title={TIME SERIES SEGMENTATION AS A DISCOVERY TOOL - A Case Study of the US and Japanese Financial Markets},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={52-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003653700520063},
isbn={978-989-8425-79-9},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - TIME SERIES SEGMENTATION AS A DISCOVERY TOOL - A Case Study of the US and Japanese Financial Markets
SN - 978-989-8425-79-9
AU - Cheng Wong, J.
AU - Hui Ting Lee, G.
AU - Zhang, Y.
AU - Shyr Yim, W.
AU - Paulo Fornia, R.
AU - Yuan Xu, D.
AU - Liang Kok, J.
AU - Ann Cheong, S.
PY - 2011
SP - 52
EP - 63
DO - 10.5220/0003653700520063

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