Adaptive, Hands-Off Stream Mining Spiros Papadimitriou, Anthony Brockwell, and Christos Faloutsos   View Paper (PDF)   Return to Data Quality, Data Mining (Session B5) Abstract . Updates are performed in constant time, using logarithmic space. Ex- isting, state of the art forecasting methods (SARIMA, GARCH, etc) fall short on one or more of these requirements. To the best of our knowledge, AWSOM is the first method that has all the above characteristics. Experiments on real and synthetic datasets demonstrate that AWSOM discovers mean- ingful patterns over long time periods. Thus, the patterns can also be used to make long- range forecasts, which are notoriously difficult to perform. In fact, AWSOM outperforms manually set up auto-regressive models, both in terms of long-term pattern detection and modeling, as well as by at least 10× in re- source consumption. ©2004 Association for Computing Machinery