08:30 - 08:45 Introductory Remarks 08:45 - 09:00 Overview of AI Approaches to Time-series Analysis 09:00 - 10:10 Technical Session 1 09:00 - 09:45 An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback (Keogh and Pazzani) 09:45 - 10:10 Discovering rules for clustering and predicting asynchronous events (Oates, Jensen and Cohen) 10:10 - 10:30 Coffee Break 10:30 - 12:05 Technical Session 2 10:30 - 11:15 Filtering techniques for rapid user classification (Lane) 11:15 - 11:40 Early prediction of electric power system blackouts by temporal machine learning (Geurts and Wehenkel) 11:40 - 12:05 Learning to predict rare events in categorical time-series data (Weiss and Hirsh) 12:05 - 01:15 LUNCH 01:15 - 02:15 Invited Talk: Leslie Kaelbling 02:15 - 02:30 Break 02:30 - 03:30 Invited Talk: Padhraic Smyth 03:30 - 04:10 General Discussion 04:10 - 04:30 Coffee Break 04:30 - 05:20 Technical Session 3 04:30 - 04:55 Learning in time ordered domains with hidden changes in context (Harries, Horn and Sammut) 04:55 - 05:20 Predicting sequences of user actions (Davison and Hirsh) 05:20 - 05:45 Discussion and Wrap-up