Useful Pattern Mining on Time Series - Applications in the Stock Market

Nikitas Goumatianos, Ioannis T. Christou, Peter Lindgren

2013

Abstract

We present the architecture of a “useful pattern” mining system that is capable of detecting thousands of different candlestick sequence patterns at the tick or any higher granularity levels. The system architecture is highly distributed and performs most of its highly compute-intensive aggregation calculations as complex but efficient distributed SQL queries on the relational databases that store the time-series. We present initial results from mining all frequent candlestick sequences with the characteristic property that when they occur then, with an average at least 60% probability, they signal a 2% or higher increase (or, alternatively, decrease) in a chosen property of the stock (e.g. close-value) within a given time-window (e.g. 5 days). Initial results from a first prototype implementation of the architecture show that after training on a large set of stocks, the system is capable of finding a significant number of candlestick sequences whose output signals (measured against an unseen set of stocks) have predictive accuracy which varies between 60% and 95% depended on the type of pattern.

Paper Citation

in Harvard Style

Goumatianos N., T. Christou I. and Lindgren P. (2013). Useful Pattern Mining on Time Series - Applications in the Stock Market . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 608-612. DOI: 10.5220/0004334106080612

in Bibtex Style

@conference{icpram13,
author={Nikitas Goumatianos and Ioannis T. Christou and Peter Lindgren},
title={Useful Pattern Mining on Time Series - Applications in the Stock Market},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={608-612},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004334106080612},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Useful Pattern Mining on Time Series - Applications in the Stock Market
SN - 978-989-8565-41-9
AU - Goumatianos N.
AU - T. Christou I.
AU - Lindgren P.
PY - 2013
SP - 608
EP - 612
DO - 10.5220/0004334106080612