{"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/tempdisagg-a-python-framework-for-temporal","title":"tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data","arxiv_id":"2503.22054","date":"2025-03-28","proceeding":null,"authors":["Jaime Vera-Jaramillo"],"abstract":"tempdisagg is a modern, extensible, and production-ready Python framework for temporal disaggregation of time series data. It transforms low-frequency aggregates into consistent, high-frequency estimates using a wide array of econometric techniques-including Chow-Lin, Denton, Litterman, Fernandez, and uniform interpolation-as well as enhanced variants with automated estimation of key parameters such as the autocorrelation coefficient rho. The package introduces features beyond classical methods, including robust ensemble modeling via non-negative least squares optimization, post-estimation correction of negative values under multiple aggregation rules, and optional regression-based imputation of missing values through a dedicated Retropolarizer module. Architecturally, it follows a modular design inspired by scikit-learn, offering a clean API for validation, modeling, visualization, and result interpretation.","url_abs":"https://arxiv.org/abs/2503.22054v1","url_pdf":"https://arxiv.org/pdf/2503.22054v1.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":"tempdisagg-a-python-framework-for-temporal","repo_url":"https://github.com/jaimevera1107/tempdisagg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}