tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913763529064448 |
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| author | Vera-Jaramillo, Jaime |
| author_facet | Vera-Jaramillo, Jaime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data Vera-Jaramillo, Jaime Econometrics Computation Machine Learning I.2.6 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. |
| title | tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data |
| topic | Econometrics Computation Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2503.22054 |