| Name | tsdisagg JSON |
| Version |
1.3
JSON |
| download |
| home_page | None |
| Summary | Statistical tools for converting low frequency time series data to higher frequencies |
| upload_time | 2024-08-30 11:30:40 |
| 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 of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
time series
decomposition
datetime
econometrics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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
To install, use
`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
For example usage, please see the `examples.ipynb` notebook. `tsdisagg` depends heavily on `pandas` to handle time reindexing, so the user is advised to read the associated Pandas documentation, especially as it relates to setting frequencies.
## To-do:
1. Refactor codebase to use `statsmodels` model and results objects, as well as `.fit()` api
2. Add missing interpolation methods relative to `timedisagg` (Fernandez, min RSS objective functions)
3. Add support for finer time frequencies (weekly, daily, hourly)
Raw data
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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\nTo install, use\n`pip install tsdisagg`\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\nFor example usage, please see the `examples.ipynb` notebook. `tsdisagg` depends heavily on `pandas` to handle time reindexing, so the user is advised to read the associated Pandas documentation, especially as it relates to setting frequencies.\n\n\n## To-do:\n1. Refactor codebase to use `statsmodels` model and results objects, as well as `.fit()` api\n2. Add missing interpolation methods relative to `timedisagg` (Fernandez, min RSS objective functions)\n3. Add support for finer time frequencies (weekly, daily, hourly)\n",
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