{"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/time-series-using-exponential-smoothing-cells","title":"Time Series Using Exponential Smoothing Cells","arxiv_id":"1706.02829","date":"2017-06-09","proceeding":null,"authors":["Avner Abrami","Aleksandr Y. Aravkin","Younghun Kim"],"abstract":"Time series analysis is used to understand and predict dynamic processes,\nincluding evolving demands in business, weather, markets, and biological\nrhythms. Exponential smoothing is used in all these domains to obtain simple\ninterpretable models of time series and to forecast future values. Despite its\npopularity, exponential smoothing fails dramatically in the presence of\noutliers, large amounts of noise, or when the underlying time series changes.\n  We propose a flexible model for time series analysis, using exponential\nsmoothing cells for overlapping time windows. The approach can detect and\nremove outliers, denoise data, fill in missing observations, and provide\nmeaningful forecasts in challenging situations. In contrast to classic\nexponential smoothing, which solves a nonconvex optimization problem over the\nsmoothing parameters and initial state, the proposed approach requires solving\na single structured convex optimization problem. Recent developments in\nefficient convex optimization of large-scale dynamic models make the approach\ntractable. We illustrate new capabilities using synthetic examples, and then\nuse the approach to analyze and forecast noisy real-world time series. Code for\nthe approach and experiments is publicly available.","url_abs":"http://arxiv.org/abs/1706.02829v4","url_pdf":"http://arxiv.org/pdf/1706.02829v4.pdf","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":"time-series-using-exponential-smoothing-cells","repo_url":"https://github.com/UW-AMO/TimeSeriesES-Cell","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}