An experimental evaluation of similarity measures for uncertain time series

Abstract

Uncertain time series analysis is important in applications such as wireless sensor networks and location-based services. This has been the subject of some recent studies, and a number of solution techniques have been proposed for similarity search problems. We classify the proposed similarity measures into deterministic, which returns a value, and probabilistic, which returns a random variable. By means of our classification, we present an overview of the proposed similarity measures and evaluate them experimentally. We conducted a comprehensive performance evaluation of these techniques through numerous experiments using the well-known real-life UCR benchmark data. As the computational complexity of some of these similarity measures was very high, we devised an effective sampling-based heuristic method to complete the experiments which could not be done before. The results of our experimental evaluation and comparison provide useful insights and guidelines for researchers and practitioners in similarity search and analysis of uncertain time series data.