{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nearest-advocate-a-novel-event-based-time","title":"Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization","arxiv_id":null,"date":"2024-04-05","proceeding":"EURASIP Journal of Advanced Signal Processing 2024 4","authors":["Christoph Schranz","Sebastian Mayr","Severin Bernhart","Christina Halmich"],"abstract":"Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.","url_abs":"https://link.springer.com/article/10.1186/s13634-024-01143-1","url_pdf":"https://link.springer.com/article/10.1186/s13634-024-01143-1","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nearest-advocate-a-novel-event-based-time","repo_url":"https://github.com/iot-salzburg/nearest-advocate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[{"method_slug":"dtw","method_name":"DTW"},{"method_slug":"nearestadvocate","method_name":"NearestAdvocate"}],"datasets_introduced":[],"methods_introduced":[{"slug":"nearestadvocate","name":"NearestAdvocate","full_name":"Nearest Advocate"}],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}