Time Series Motif Discovery: A Comprehensive Evaluation

Motif Discovery involves identifying recurring patterns and locating their occurrences within a time series without prior knowledge about their shape or location. In practice, Motif Discovery faces several data-related challenges, leading to various definitions of the problem and multiple algorithms addressing these challenges to different extents. However, there has been no systematic evaluation and comparison of these diverse approaches. Consequently, this paper presents a comprehensive literature review covering data-related challenges, motif definitions, and algorithms. We also analyze the strengths and limitations of algorithms carefully chosen to represent the literature diversity. The analysis is structured around key research questions identified from our review. Our experimental findings provide practical guidelines for selecting Motif Discovery algorithms suitable for a given task and suggest directions for future research.