| Name | tsdisagg JSON |
| Version |
1.3.2
JSON |
| download |
| home_page | None |
| Summary | Temporal Disaggregation of Time Series Data in Python |
| upload_time | 2025-10-13 23:49:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License
Copyright (c) 2022 jessegrabowski
Permission is hereby granted, free of charge, to any person obtaining a copy
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| keywords |
datetime
decomposition
econometrics
time series
|
| VCS |
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# tsdisagg
Tools for converting low time series data to high frequency, based on the R package `tempdisagg`, and espeically the accompanying paper by [Sax and Steiner 2013](https://journal.r-project.org/archive/2013-2/sax-steiner.pdf).
`tsdisagg` allows the user to convert low frequency time series data (e.g., yearly or quarterly) to a higher frequency (e.g., quarterly or monthly) in a way that preserves desired aggregate statistics in the high frequency data. It should, for example, sum back to the original low-frequency data.
In addition, regression-based methods are also implemented that allow the user to supply "indicator series", allowing variation from correlated high-frequency time series to be imputed into the low frequency data.
If you have any questions or issues, please open a thread. Pull requests to add features or fix bugs are welcome. Please clone the repository locally to have access to the testing suite.
## Installation
`tsdisagg` is distributed on `conda-forge`. To install, use conda/mamba:
```
conda install -c conda-forge tsdisagg
```
Or, of course, you can install using pip:
```
pip install tsdisagg
```
## Current Features
Currently, only conversion between yearly, quarterly, and monthly data is supported. Conversion to lower frequencies is non-trivial due to the calendar math that needs to be added, but this is on my to-do list.
The following interpolation methods have been implemented:
Single series, non-parametric methods:
- Denton
- Denton-Cholette
Multiseries, regression-based methods:
- Chow-Lin
- Litterman
## Examples
Disaggregate a timeseries using the univariate Denton-Cholette method:
```python
import pandas as pd
from tsdisagg import disaggregate_series
from tsdisagg.datasets import load_data
# Load example data
sales_a = load_data("annual_sales")
# Disaggregate from annual to quarterly using Denton-Cholette method
sales_q_dc = disaggregate_series(
sales_a.resample("YS").last(), # Use `.resample` to ensure the frequency is set correctly
target_freq="QS", # Desired output frequency
method="denton-cholette", # Disaggregation method
agg_func="sum", # Sales are flow data, so we want the quarters to sum back to the annual data
h=1, # Differencing order (1 in this case to preserve the trend)
)
```
Disaggregate a timeseries using the multivariate Chow-Lin method with an indicator series:
```python
import pandas as pd
from tsdisagg import disaggregate_series
from tsdisagg.datasets import load_data
# Load example data
sales_a = load_data("annual_sales")
exports_q = load_data("quarterly_exports")
# Disaggregate from annual to quarterly using Chow-Lin method with quarterly sales as indicator
sales_q_chow_lin = disaggregate_series(
sales_a.resample("YS").last(), # Target series, annual frequency
exports_q.assign(intercept=1), # Indicator matrix. We can have as many series as we want here; so we use
# Exports at quarterly frequency, plus a deterministic intercept term.
method="chow-lin", # Disaggregation method
agg_func="sum", # Sales are flow data, so we want the quarters to sum back to the annual data
optimizer_kwargs={"method": "powell"}, # Additional arguments to the optimizer
)
```
# Citing `tsdisagg`
If you use `tsdisagg` in your research, please use the following citation:
```bibtex
@software{tsdisagg,
author = {Jesse Grabowski},
title = {tsdisagg: Temporal Disaggregation of Time Series Data in Python},
version = {0.1.0},
url = {https://github.com/jessegrabowski/tsdisagg},
howpublished = {GitHub},
year = {2025},
}
```
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"description": "# tsdisagg\nTools for converting low time series data to high frequency, based on the R package `tempdisagg`, and espeically the accompanying paper by [Sax and Steiner 2013](https://journal.r-project.org/archive/2013-2/sax-steiner.pdf).\n\n`tsdisagg` allows the user to convert low frequency time series data (e.g., yearly or quarterly) to a higher frequency (e.g., quarterly or monthly) in a way that preserves desired aggregate statistics in the high frequency data. It should, for example, sum back to the original low-frequency data.\n\nIn addition, regression-based methods are also implemented that allow the user to supply \"indicator series\", allowing variation from correlated high-frequency time series to be imputed into the low frequency data.\n\nIf you have any questions or issues, please open a thread. Pull requests to add features or fix bugs are welcome. Please clone the repository locally to have access to the testing suite.\n\n## Installation\n`tsdisagg` is distributed on `conda-forge`. To install, use conda/mamba:\n\n```\nconda install -c conda-forge tsdisagg\n```\n\nOr, of course, you can install using pip:\n\n```\npip install tsdisagg\n```\n\n## Current Features\nCurrently, only conversion between yearly, quarterly, and monthly data is supported. Conversion to lower frequencies is non-trivial due to the calendar math that needs to be added, but this is on my to-do list.\n\nThe following interpolation methods have been implemented:\n\nSingle series, non-parametric methods:\n- Denton\n- Denton-Cholette\n\nMultiseries, regression-based methods:\n- Chow-Lin\n- Litterman\n\n\n## Examples\n\nDisaggregate a timeseries using the univariate Denton-Cholette method:\n```python\nimport pandas as pd\nfrom tsdisagg import disaggregate_series\nfrom tsdisagg.datasets import load_data\n\n# Load example data\nsales_a = load_data(\"annual_sales\")\n\n\n# Disaggregate from annual to quarterly using Denton-Cholette method\nsales_q_dc = disaggregate_series(\n sales_a.resample(\"YS\").last(), # Use `.resample` to ensure the frequency is set correctly\n target_freq=\"QS\", # Desired output frequency\n method=\"denton-cholette\", # Disaggregation method\n agg_func=\"sum\", # Sales are flow data, so we want the quarters to sum back to the annual data\n h=1, # Differencing order (1 in this case to preserve the trend)\n)\n```\n\nDisaggregate a timeseries using the multivariate Chow-Lin method with an indicator series:\n```python\nimport pandas as pd\nfrom tsdisagg import disaggregate_series\nfrom tsdisagg.datasets import load_data\n\n# Load example data\nsales_a = load_data(\"annual_sales\")\nexports_q = load_data(\"quarterly_exports\")\n\n# Disaggregate from annual to quarterly using Chow-Lin method with quarterly sales as indicator\nsales_q_chow_lin = disaggregate_series(\n sales_a.resample(\"YS\").last(), # Target series, annual frequency\n exports_q.assign(intercept=1), # Indicator matrix. We can have as many series as we want here; so we use\n # Exports at quarterly frequency, plus a deterministic intercept term.\n method=\"chow-lin\", # Disaggregation method\n agg_func=\"sum\", # Sales are flow data, so we want the quarters to sum back to the annual data\n optimizer_kwargs={\"method\": \"powell\"}, # Additional arguments to the optimizer\n)\n```\n\n# Citing `tsdisagg`\nIf you use `tsdisagg` in your research, please use the following citation:\n\n```bibtex\n@software{tsdisagg,\nauthor = {Jesse Grabowski},\ntitle = {tsdisagg: Temporal Disaggregation of Time Series Data in Python},\nversion = {0.1.0},\nurl = {https://github.com/jessegrabowski/tsdisagg},\nhowpublished = {GitHub},\nyear = {2025},\n}\n```\n",
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