Name
Abstract | ||
|---|---|---|
In 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 inter-relationship 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. |
Year | Venue | Keywords |
|---|---|---|
2006 | DMIN | associative classification,stock data mining,time series analysis,association rule mining,time series,association analysis,majority voting,data mining |
Field | DocType | Citations |
Data mining,Associative property,Pattern recognition,Computer science,Artificial intelligence | Conference | 7 |
PageRank | References | Authors |
0.67 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jo Ting | 1 | 7 | 0.67 |
| Tak-chung Fu | 2 | 407 | 21.29 |
| Fu-lai Chung | 3 | 244 | 34.50 |