  In the last years there has been a considerable increase in the availability
of continuous sensor measurements in a wide range of application domains, such
as Location-Based Services (LBS), medical monitoring systems, manufacturing
plants and engineering facilities to ensure efficiency, product quality and
safety, hydrologic and geologic observing systems, pollution management, and
others. Due to the inherent imprecision of sensor observations, many
investigations have recently turned into querying, mining and storing uncertain
data. Uncertainty can also be due to data aggregation, privacy-preserving
transforms, and error-prone mining algorithms. In this study, we survey the
techniques that have been proposed specifically for modeling and processing
uncertain time series, an important model for temporal data. We provide an
analytical evaluation of the alternatives that have been proposed in the
literature, highlighting the advantages and disadvantages of each approach, and
further compare these alternatives with two additional techniques that were
carefully studied before. We conduct an extensive experimental evaluation with
17 real datasets, and discuss some surprising results, which suggest that a
fruitful research direction is to take into account the temporal correlations
in the time series. Based on our evaluations, we also provide guidelines useful
for the practitioners in the field.
