An Efficient System for Detecting Outliers from Financial Time Series

In this paper, we develop an efficient system to detect outliers from real-life financial time series comprising of security prices. Our system consists of a data mining algorithm and a statistical algorithm. When applying each of these two algorithms individually, we observed its strengths and weaknesses. To overcome the weaknesses of the two algorithms, we combine the algorithms together. By so doing, we efficiently detect outliers from the financial time series. Moreover, the resulting (processed) datasets can then be used as input for some financial models used in forecasting future security prices or in predicting future market behaviour. This shows an alternative role of our outlier detection system—serving as a pre-processing step for other financial models.