2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI) (2012)
Nov. 16, 2012 to Nov. 18, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAAI.2012.42
In this paper we present a model to predict the stock trend based on a combination of sequential chart pattern, K-Means and AprioriAll algorithm. The stock price sequence is truncated to charts by sliding window. Then the charts are clustered by K-Means algorithm to form chart patterns. Therefore, the charts form chart pattern sequences, and frequent patterns in the sequences can be extracted by AprioriAll algorithm. The existence of frequent patterns implies that some specific market behaviors often show accompanied, thus the corresponding trend can be predicted. Experiment results show that the proposed system can produce better index return with fewer trade. Its annualized return is also better than award winning mutual funds. Therefore, the proposed method makes profits on the real market, even in a long-term usage.
economic indicators, Haar transforms, pattern clustering, profitability, stock markets, time series
Y. Wu, K. Wu and H. Lee, "Stock Trend Prediction by Sequential Chart Pattern via K-Means and AprioriAll Algorithm," 2012 Conference on Technologies and Applications of Artificial Intelligence(TAAI), Tainan, Taiwan Taiwan, 2013, pp. 176-181.