Mining of Stock Data: Intra- and Inter-Stock Pattern Associative Classification

this paper, a pattern-based stock data mining approach which transforms the numeric stock data to symbolic sequences, carries out sequential and non-sequential association analysis and uses the mined rules in classifying/predicting the further price movements is proposed. Two formulations of the problem are considered. They are intra-stock mining which focuses on finding frequently appearing patterns for the stock time series itself and inter-stock mining which discovers the strong interrelationship among several stocks. Three different methods are proposed for carrying out associative classification/prediction, namely, Best Confidence, Maximum Window Size and Majority Voting. They select the mined rule(s) and make the final prediction. A modified Apriori algorithm is also proposed to mine the frequent symbolic sequences in intra-stock mining and the frequent symbol-sets in inter-stock mining. Various experimental results are reported.