tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data

• URL: http://arxiv.org/abs/2503.22054v1
• Date: Fri, 28 Mar 2025 00:15:52 GMT
• Title: tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data
• Authors: Jaime Vera-Jaramillo,
• Abstract summary: tempdisagg is a Python framework for temporal disaggregation of time series data.<n>It transforms low-frequency aggregates into consistent, high-frequency estimates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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