Title: tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data Show Chinese title

Title: tempdisagg：一种用于时间序列数据时间 disaggregation 的 Python 框架

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. Show Chinese abstract

Abstract: tempdisagg 是一个现代、可扩展且适用于生产的 Python 框架，用于时间序列数据的时间分解。 它使用各种计量经济学技术将低频汇总数据转换为一致的高频估计值，包括 Chow-Lin、Denton、Litterman、Fernandez 和均匀插值法，以及增强变体，可以自动估计关键参数，如自相关系数 rho。 该包引入了超越传统方法的功能，包括通过非负最小二乘优化的鲁棒集成建模，以及在多种汇总规则下对负值进行估计后校正，以及通过专用 Retropolarizer 模块进行可选的基于回归的缺失值插补。 在架构上，它遵循受 scikit-learn 启发的模块化设计，提供了一个干净的 API，用于验证、建模、可视化和结果解释。