An experimental study of existing tools for outlier detection and cleaning in trajectories

TL;DR

This study evaluates ten open-source outlier detection tools for trajectory data, comparing their efficiency and accuracy to guide users in selecting appropriate methods for data cleaning.

Contribution

It introduces a ground-truth establishment method and provides a comprehensive comparison of existing libraries and algorithms for trajectory outlier detection.

Findings

Identifies the most effective tools for outlier detection in trajectories.

Provides a classification and survey of state-of-the-art algorithms.

Offers practical guidance for data preprocessing in trajectory analysis.

Abstract

Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory. We experiment with ten open-source libraries to comprehensively evaluate available tools, comparing their efficiency and accuracy in identifying and cleaning outliers. This experiment considers the libraries as they are offered to end users, with real-world applicability. We compare existing outlier detection libraries, introduce a method for establishing ground-truth, and aim to guide users in choosing the most appropriate tool for their specific outlier detection needs. Furthermore, we survey the state-of-the-art algorithms for outlier detection and classify them into five types: Statistic-based methods, Sliding window algorithms, Clustering-based methods, Graph-based methods, and Heuristic-based methods. Our research provides insights into these libraries' performance and contributes to developing data preprocessing and outlier detection methodologies.