In this paper, we consider complex pattern matching over event data generated from error-prone sources such as low-cost wireless motes, RFID. Such data are often imprecise in both their values and their timestamps. While there are existing works addressing the problem of spatial uncertainty (i.e. the uncertainty of the data values), relatively little attention has been paid to the problem of temporal uncertainty (i.e. the uncertainty of the event timestamps). As a step to fill this gap, we formulate the problem of matching complex sequence patterns over time-series data with temporal uncertainty and propose a new indexing structure to organize the information of the uncertain sequences and a set of efficient pattern query processing algorithms. We conduct an extensive experimental study on both synthetic and real datasets. The results indicate that the query processing algorithms based on our index structure can dramatically improve the query performance.