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

arXiv: https://arxiv.org/abs/2503.22054 · HTML: https://mlsanity.com/paper/2503.22054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Daily Forecasting for Annual Time Series Datasets Using Similarity-Based Machine Learning Methods: A Case Study in the Energy Market](https://mlsanity.com/paper/2511.05556/) (2025)

## Abstract (arXiv)

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.