Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Time-series similarity problems and well-separated geometric sets
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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Predicting stock market movements is always difficult. Investors try to guess a stock's behavior, but it often backfires. Thumb rules and intuition seems to be the major indicator. One approach suggested that instead of trying to predict one particular stock's movement with respect to the whole market, it may be easier to predict a stock A's movement based on another stock B's movement; the reason being that A may get affected by B after B's movement, giving the investor invaluable time advantage. Evidently, it would be very useful if a general framework can be introduced that can predict such dependence based on any user defined criterion. A previous paper laid a basic framework for a single event based criterion, but that was not enough where multiple criteria were involved. This paper gives a general framework for multiple events. We show that it is possible to encode a time series as a string, where the final representation depends on the user defined criterion. Then finding string distances between two such encoded time series can effectively measure dependence. We show that this technique is more powerful than the 'Pairs Trading strategy' as varied user defined criterion can be handled while detecting similarity. We apply our technique with one practical user defined criterion. To the best of our knowledge, this is the first attempt to find similarity between stock trends based on user defined multiple event criteria.