Distribution based data filtering for financial time series forecasting

Authors:
Goce Ristanoski;James Bailey
Affiliations:
The University of Melbourne, Melbourne, Australia;The University of Melbourne, Melbourne, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

Changes in the distribution of financial time series, particularly stock market prices, can happen at a very high frequency. Such changes make the prediction of future behavior very challenging. Application of traditional regression algorithms in this scenario is based on the assumption that all data samples are equally important for model building. Our work examines the use of an alternative data pre-processing approach, whereby knowledge of distribution changes is used to pre-filter the training dataset. Experimental results indicate that this simple and efficient technique can produce effective results and obtain improvements in prediction accuracy when used in conjunction with a range of forecasting techniques.