tsdisagg 1.3.1

Temporal Disaggregation of Time Series Data in Python

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.

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

Disaggregate a timeseries using the univariate Denton-Cholette method:

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:

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:

@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},
}