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Author: Karsten Martiny

Affiliation: Hamburg University of Technology, Germany

ISBN: 978-989-8565-29-7

Keyword(s): Candlestick Patterns, Time Series Analysis, Market Forecasting, Pattern Discovery, Information Extraction, Unsupervised Learning, Hierarchical Clustering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Candlestick charts are a visually appealing method of presenting price movements of securities. It has been developed in Japan centuries ago. The depiction of movements as candlesticks tends to exhibit recognizable patterns that allow for predicting future price movements. Common approaches of employing candlestick analysis in automatic systems rely on a manual a-priori specification of well-known patterns and infer prognoses upon detection of such a pattern in the input data. A major drawback of this approach is that the performance of such a system is limited by the quality and quantity of the predefined patterns. This paper describes a novel method of automatically discovering significant candlestick patterns from a time series of price data and thereby allows for an unsupervised machine-learning task of predicting future price movements.

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Paper citation in several formats:
Martiny, K. (2012). Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 145-150. DOI: 10.5220/0004107701450150

@conference{kdir12,
author={Karsten Martiny.},
title={Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={145-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004107701450150},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements
SN - 978-989-8565-29-7
AU - Martiny, K.
PY - 2012
SP - 145
EP - 150
DO - 10.5220/0004107701450150

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