2025/03/28 by Jaime Vera-Jaramillo, Vera-Jaramillo, Jaime · 1 citation
Computer Science · Mathematics · #Computation (stat.CO) #Data Analysis with R #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #I.2.6 #Machine Learning (stat.ML) #Statistical and numerical algorithms #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2503.22054
openalex publication_date 2025/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.