Stock Trading Rule Discovery based on temporal data mining

One of the major tasks in stock market analysis is the discovery of specific events that give rise to a particular event. In this research we emphasize on temporal data mining with a time dimensional approach. This has led us to the discovery of sequential continuous patterns. The patterns serve as rules that enable us to determine the occurrence of an event on a particular stock-transaction day. In our paper, we have proposed and implemented the STRDTM (Stock Trading Rule Discovery by Temporal Mining) algorithm with real life data from Dhaka Stock Exchange as input.