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**Harvard**

Wahde, M. (2016) *A framework for optimization of pattern sets for financial time series prediction*.

** BibTeX **

@conference{

Wahde2016,

author={Wahde, Mattias},

title={A framework for optimization of pattern sets for financial time series prediction},

booktitle={Proceedings of 2016 SAI Intelligent Systems Conference (IntelliSys)},

isbn={978-1-5090-1121-6},

pages={31-38},

abstract={Abstract—In this paper, a framework is introduced for generating
human-interpretable structures, here called pattern sets, for
short-term prediction of financial time series. The optimization
is carried out using an evolutionary algorithm, which is able to
modify both the structure and the parameters of the evolving
pattern sets. The framework has been applied in two different
modes: A tuning mode, in which the user provides a starting
point in the form of loosely defined pattern set, and a discovery
mode, in which the starting points consist of random pattern sets.
The best results were obtained in the tuning mode, for which the
top-performing pattern sets gave strongly statistically significant
results in excess of one-day market returns (p−values below
0.0007 and, in many cases, even below 0.0001) over validation
data (not used during optimization) for two data sets, involving
stocks with large and small market capitalization, respectively.
The average one-day returns ranged from 0.518 to 1.147%, with
one-day Sharpe ratios ranging from 0.138 to 0.258.},

year={2016},

keywords={Financial prediction, Time series analysis, Evolutionary computation},

}

** RefWorks **

RT Conference Proceedings

SR Print

ID 244595

A1 Wahde, Mattias

T1 A framework for optimization of pattern sets for financial time series prediction

YR 2016

T2 Proceedings of 2016 SAI Intelligent Systems Conference (IntelliSys)

SN 978-1-5090-1121-6

SP 31

OP 38

AB Abstract—In this paper, a framework is introduced for generating
human-interpretable structures, here called pattern sets, for
short-term prediction of financial time series. The optimization
is carried out using an evolutionary algorithm, which is able to
modify both the structure and the parameters of the evolving
pattern sets. The framework has been applied in two different
modes: A tuning mode, in which the user provides a starting
point in the form of loosely defined pattern set, and a discovery
mode, in which the starting points consist of random pattern sets.
The best results were obtained in the tuning mode, for which the
top-performing pattern sets gave strongly statistically significant
results in excess of one-day market returns (p−values below
0.0007 and, in many cases, even below 0.0001) over validation
data (not used during optimization) for two data sets, involving
stocks with large and small market capitalization, respectively.
The average one-day returns ranged from 0.518 to 1.147%, with
one-day Sharpe ratios ranging from 0.138 to 0.258.

LA eng

OL 30