Developed and documented by John Stephen Joseph Arul Selvam
This package implements time disaggregation models inspired heavily by R's tempdisagg package. The package currently has the following methods implemented:
- Chow-Lin (max. log)
- Chow-Lin (min. RSS)
- Litterman (max. log)
- Litterman (min. RSS)
- Chow-Lin (dynamic max. log)
- Chow-Lin (dynamic min. RSS)
Details on these models as well as the underlying math are available at this article written by the R package's authors.
These models ensure that the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. The conversion type can also be adjusted by the function call.
The traditional example of disaggregating sales.a against exports.q available under the R package's vignette is discussed here.
The input dataset to the TempDisagg's object call should have the following format:
index grain X y
0 1972 1 1432.63900 NaN
1 1972 2 1456.89100 NaN
2 1972 3 1342.56200 NaN
3 1972 4 1539.39400 NaN
4 1973 1 1535.75400 NaN
5 1973 2 1578.45800 NaN
6 1973 3 1574.72400 NaN
7 1973 4 1652.17100 NaN
8 1974 1 2047.83400 NaN
9 1974 2 2117.97100 NaN
10 1974 3 1925.92600 NaN
11 1974 4 1798.19000 NaN
12 1975 1 1818.81700 136.702329
13 1975 2 1808.22500 136.702329
14 1975 3 1649.20600 136.702329
15 1975 4 1799.66500 136.702329
16 1976 1 1985.75300 151.056074
17 1976 2 2064.66300 151.056074
18 1976 3 1856.38700 151.056074
19 1976 4 1919.08700 151.056074
.. ... ... ... ...
152 2010 1 19915.79514 988.309676
153 2010 2 19482.48000 988.309676
154 2010 3 18484.64900 988.309676
155 2010 4 18026.46869 988.309676
156 2011 1 19687.52100 NaN
157 2011 2 18913.06608 NaN
The index column holds the low-frequency time periods from which high-frequency time series (grain) are generated.
y is the data to be disaggregated and X is the high-frequency regressor. In this case, X is the quarter-level
export numbers while y holds yearly sales numbers. This data is available under tests/sample_data.csv along with a demo-run.py.
Note that the current package does not accept Nan values in any of these columns except for valid backcasting and forecasting periods in the y column.
For example, in the above case, the package is expected to provide backcasted values for the 12 quarters from 1972 to 1974 and 2 forecasted quarters for 2011.
The file for this walkthrough is available as tests/demo_run.py. Ensure to pip install timedisagg before running.
import pandas as pd
from timedisagg.td import TempDisagg
expected_dataset = pd.read_csv("./tests/sample_data.csv")
td_obj = TempDisagg(conversion="sum", method="chow-lin-maxlog")
final_disaggregated_output = td_obj(expected_dataset)
print(final_disaggregated_output.head()
Output:
index grain X y y_hat
0 1972 1 1432.639 NaN 21.656879
1 1972 2 1456.891 NaN 22.219737
2 1972 3 1342.562 NaN 20.855413
3 1972 4 1539.394 NaN 23.937916
4 1973 1 1535.754 NaN 24.229008
y_hat holds the disaggregated values of y.
Detailed documentation will follow, but for now the different methods can be referred by the following tags:
"chow-lin-maxlog"
"chow-lin-minrss-ecotrim"
"chow-lin-minrss-quilis"
"litterman-maxlog"
"litterman-minrss"
"dynamic-maxlog"
"dynamic-minrss"
And conversions can be selected by sum,average,first and last as available in the R package.
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