The analysis of events ordered over time and the discovery of significant hidden relationships from this temporal data is becoming the concern of the information society. Using temporal data as temporal sequences without any preprocessing fails to find key features of these data. Therefore, before applying mining techniques, an appropriate representation of temporal sequences is needed. Our representation of time series can be used in different fields, such as aviation science and earth science, and can also be applied to, for instance, Temporal Web Mining (TWM) [1], [2], [3], [4]. Our representation of time series aims at improving the possibility of specifying and finding an important occurrence. In our new concept, we use data band ranges and areas in order to determine the importance or the weight of a segment. According to the closeness of a segment to a data band range, this representation of time series can help to find a significant event.

This paper focuses on our representation of time series.