Skip to Main Content
Mining textual documents and time series concurrently, such as predicting the movements of stock prices based on news articles, is an emerging topic in data mining society nowadays. Previous research has already suggested that the relationship between news articles and stock prices do exist. However, all of the existing approaches are concerning in mining single time series only. The interrelationships among different stocks are not well-addressed. Mining multiple time series concurrently is not only more informative but also far more challenging. Research in such a direction is lacking. In this paper, we try to explore such an opportunity and propose a systematic framework for mining multiple time series based on Efficient Market Hypothesis.