Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Continuous categories for a mobile robot
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Genetic Programming Prediction of Stock Prices
Computational Economics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time series data mining: identifying temporal patterns for characterization and prediction of time series events
Trading strategy design in financial investment through a turning points prediction scheme
Expert Systems with Applications: An International Journal
Clustering based stocks recognition
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Sequence outlier detection based on chaos theory and its application on stock market
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
International Journal of Data Analysis Techniques and Strategies
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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.