TAILIEUCHUNG - Event Detection from Time Series Data

For small events this could be as simple as erecting a small gazebo with a couple of trestle tables for registration purposes. Larger, more elaborate events will require a fully marked out site with spaces allocated for whatever welfare facilities and entertainment you are organising. You should make your plans for these with the assumption that there may be spectators and supporters as well as participants at the start and finish, and that they may well want to enjoy any on-site facilities and entertainment you provide until the walkers return. . | Event Detection from Time Series Data Valery Guralnik Jaideep Srivastava Department of Computer Science University of Minnesota guralnik srivasta @ Abstract In the past few years there has been increased interest in using data-mining techniques to extract interesting patterns from time series data generated by sensors monitoring temporally varying phenomenon. Most work has assumed that raw data is somehow processed to generate a sequence of events which is then mined for interesting episodes. In some cases the rule for determining when a sensor reading should generate an event is well known. However if the phenomenon is ill-understood stating such a rule is difficult. Detection of events in such an environment is the focus of this paper. Consider a dynamic phenomenon whose behavior changes enough over time to be considered a qualitatively significant change. The problem we investigate is of identifying the time points at which the behavior change occurs. In the statistics literature this has been called the change-point detection problem. The standard approach has been to a apriori determine the number of change-points that are to be discovered and b decide the function that will be used for curve fitting in the interval between successive change-points. In this paper we generalize along both these dimensions. We propose an iterative algorithm that fits a model to a time segment and uses a likelihood criterion to determine if the segment should be partitioned further . if it contains a new changepoint. In this paper we present algorithms for both the batch and incremental versions of the problem and evaluate their behavior with synthetic and real data. Finally we present initial results comparing the change-points detected by the batch algorithm with those detected by people using visual inspection. 1 Introduction Sensor-based monitoring of any phenomenon creates time series data. The spacing between successive readings may be constant or varying .

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