TD-Join: Leveraging Temporal Dependencies in Time Series Joins

Time series data is pervasive across domains such as healthcare, finance, and environmental monitoring, where understanding temporal relationships between events is crucial. Traditional time series analysis primarily focuses on numerical similarity measures, often overlooking qualitative temporal relationships. This demonstration introduces TD-Join, a novel tool for temporal reasoning on time series. TD-Join enhances time series subsequence joins by integrating Allen's Algebra, a widely adopted framework for temporal reasoning. Our approach enables users to efficiently identify, query, and interpret temporal relationships such as before, overlaps, meets and equal over similar time series subsequences. By leveraging such relations, our algorithm facilitates more interpretable and robust analysis, improving performance and augmenting the results set, facilitating decision-making processes. The demonstration will showcase real-world scenarios, i.e., healthcare and financial analyses. Attendees will experience interactive visualizations and real-time query capabilities allowing more explainable and insightful time series analysis.