Uncertain Time-Series Similarity: Return to the Basics

TL;DR

This paper surveys and evaluates methods for modeling and processing uncertain time series, highlighting the importance of considering temporal correlations and providing practical guidelines based on extensive experiments with real data.

Contribution

It offers a comprehensive analysis of existing techniques for uncertain time-series data, comparing their advantages and disadvantages, and introduces new insights into the significance of temporal correlations.

Findings

Temporal correlations are crucial for effective uncertain time-series analysis.

Some existing methods perform better when temporal correlations are considered.

Guidelines for practitioners are provided based on experimental results.

Abstract

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.