Paper Info
Title
Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events
Abstract
The novel Time Series Data Mining (TSDM) framework is applied to analyzing financial time series. The TSDM framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. This contrasts with other time series analysis techniques, which typically characterize and predict all observations. The TSDM framework and concepts are reviewed, and the applicable TSDM method is discussed. Finally, the TSDM method is applied to time series generated by a basket of financial securities. The results show that statistically significant temporal patterns that are both characteristic and predictive of events in financial time series can be identified.
Year
DOI
Venue
2000
10.1007/3-540-45244-3_5
TSDM
Keywords
Field
DocType
identifying temporal patterns,time series,time series event,tsdm method,financial security,time series analysis technique,tsdm framework,financial time series events,applicable tsdm method,time series data,financial time series,tsdm framework adapts,time series analysis,statistical significance,data mining
Time series,Data mining,Time series data mining,Efficient-market hypothesis,Computer science,Contrast (statistics),Finance
Conference
ISBN
Citations 
PageRank 
3-540-41773-7
15
1.25
References 
Authors
5
1
Name
Order
Citations
PageRank
Richard J. Povinelli122520.40