Inproceedings,

Using data mining techniques for detecting noises and pre-processing financial time series

, , and .
JCIS (CIEF) 2005, page 1138-1141. (2005)

Abstract

In this paper, we propose a system to detect noises and to pre-process financial time series. This novel system combines a statistical algorithm with a data mining algorithm. We implemented and tested both algorithms on real-life historical financial time series consisting of security prices with outliers. We observed the strengths and weaknesses of each of the two algorithms, and then developed a hybrid algorithm to overcome the weaknesses of the two algorithms. Consequently, the resulting (processed) datasets can be used as input for models used in forecasting future security prices and in predicting future market behaviour.

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