Similarity matching for uncertain time series: analytical and experimental comparison

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 both an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach. We additionally conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results. Based on our evaluations, we also provide guidelines useful for practitioners in the field.