ples. This summarization, or aggregation, process typically involves using a fixed time window and characterizing the behavior over this window. For example, if n unique alarms are possible, one could describe the behavior of a device over this time window using a scalar of length N. In this case each field in the scalar would contain a count of the number of times a specific alarm occurs. One may then label the constructed example based on whether a fault occurs within some other time frame, for example, within the following 5 minutes. Thus, two time windows are required. Once this encoding is complete, standard classification tools can be used to generate "rules" to predict future failures. Such an encoding scheme was used to identify chronic circuit problems (Sasisekharan, Seshadri & Weiss, 1996). The problem of reformulating time-series network events SO that conventional classification- based data mining tools can be used to identify network faults has been Data Mining In Telecommunications studied. Weiss & Hirsh (1998) view this task as an event prediction problem while Fawcett & Provost (1999) view it as an activity monitoring problem. Transforming the time-series data SO that standard classification tools can be used has several drawbacks. The most significant one is that some information will be lost in the reformulation process. For example, using the scalar-based representation just mentioned, all sequence information is lost. Timeweaver (Weiss & Hirsh, 1998) is a genetic-algorithm based data mining system that is capable of operating directly on the raw network-level time- series data (as well as other time-series data), thereby making it unnecessary to re-represent the network level data. Given a sequence of timestamped events and a target event T, Timeweaver will identify patterns that successfully predict T. Timeweaver essentially searches through the space of possible patterns, which includes sequence and temporal relationships, to find predictive patterns. The system is especially designed to per