This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence (CC BY-NC-SA 4.0): research, academic, personal and other non-commercial use is permitted with attribution; adaptations must be shared under the same licence; commercial use is not permitted. The original public source data remains under the Government Open Data License - India (GODL-India). Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.

HMIS-Bench

Licence: CC BY-NC-SA 4.0 — research and non-commercial use only; derivatives must be shared under the same licence. Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai

Access is approved automatically when the email you enter matches your Hugging Face account email and belongs to the organisation you name; other requests are reviewed manually.

A probabilistic forecasting benchmark built from India's Health Management Information System (HMIS): monthly counts that public and private health facilities report for each sub-district, April 2020 to March 2022, for 48 headline indicators of maternal health, deliveries, immunisation, family planning, disease and outpatient services. The task is to forecast each series 1, 2 and 3 months ahead from six rolling origins (August 2021 to January 2022) with nine quantiles. Four states are held out entirely, so models can be scored on sub-districts they never saw. The release ships the series, the task definitions, the forecasts of nine models and an evaluate.py that recomputes the leaderboard from those forecasts exactly.

Quickstart

import pandas as pd

# after `huggingface-cli download judicialmind/HMIS-Bench --repo-type dataset --local-dir HMIS-Bench`
bench = pd.read_parquet("HMIS-Bench/series/bench.parquet")      # or "hf://datasets/judicialmind/HMIS-Bench/series/bench.parquet"
print(bench.series_id.nunique(), "series,", len(bench), "rows")
print(bench.loc[bench.series_id == "hmis|10|188|1112|1.1|total", ["ts", "target"]].head(4).to_string(index=False))

100000 series, 2364616 rows
        ts  target
2020-04-01     0.0
2020-05-01  1721.0
2020-06-01  1207.0
2020-07-01  1255.0

With the datasets library: load_dataset("judicialmind/HMIS-Bench", "bench", split="train") (configs bench, all, district_extended).

Score the shipped forecasts

cd HMIS-Bench
pip install -r requirements.txt      # numpy, pandas, pyarrow
python evaluate.py --check          # recompute every leaderboard row from forecasts/ (about 2 minutes, 5 GB RAM)

 h               model    wql   mase  cov_80  win_rate  n_windows
 1               tirex 0.1435 0.8348  0.8278    0.5175     585323
 1            chronos2 0.1458 0.8380  0.8267       NaN     585323
 ...
checked 1377 of 1377 leaderboard rows against leaderboard.json: 0 mismatching values

Evaluate your own model

For each origin in splits.json, get the benchmark windows: make_windows(root, origin) in evaluate.py returns series_id, a 36-month context (oldest first, NaN = month not reported) and the 3 observed future values. A window exists when the series has at least 8 reported months in the context and at least one reported month in the 3 months from the origin.
Forecast 3 months with the quantiles 0.1, 0.2, ..., 0.9 and write forecasts/<your_model>/<origin>.parquet with columns series_id, origin, h, ts, q0.1 ... q0.9, mean, target (one row per series and step h = 1..3; target = the observed value from future).
Run python evaluate.py --models chronos2,<your_model> (add the other models to compare on the same windows).

A simple example (last value ± a widening normal band):

import os, sys
import numpy as np, pandas as pd
sys.path.insert(0, "HMIS-Bench")
from evaluate import make_windows, QCOLS

origin = "2021-08-01"                                                    # repeat for each origin in splits.json
w = make_windows("HMIS-Bench", origin)
last = np.array([c[np.isfinite(c)][-1] for c in w.context])             # last reported value
spread = np.array([np.nanstd(c) for c in w.context])
z = np.array([-1.2816, -0.8416, -0.5244, -0.2533, 0, 0.2533, 0.5244, 0.8416, 1.2816])
rows = []
for h in (1, 2, 3):
    q = np.maximum(last[:, None] + np.sqrt(h) * spread[:, None] * z[None, :], 0)
    df = pd.DataFrame(q, columns=QCOLS)
    df.insert(0, "series_id", w.series_id); df.insert(1, "origin", pd.Timestamp(origin).date())
    df.insert(2, "h", h); df.insert(3, "ts", (pd.Timestamp(origin) + pd.DateOffset(months=h - 1)).date())
    df["mean"] = df["q0.5"]; df["target"] = [f[h - 1] for f in w.future]
    rows.append(df)
os.makedirs("HMIS-Bench/forecasts/my_naive", exist_ok=True)
pd.concat(rows).to_parquet(f"HMIS-Bench/forecasts/my_naive/{origin}.parquet")

cd HMIS-Bench && python evaluate.py --models chronos2,my_naive    # scored on the windows both models cover

 h    model    wql   mase  cov_80  win_rate  n_windows
 1 chronos2 0.1624 0.9946  0.7843       NaN      96211
 1 my_naive 0.1673 1.0557  0.7489    0.5065      96211
 ...

Files

file	rows	content
`series/bench.parquet`	2,364,616	the benchmark: 100,000 sub-district series, 24 months each at most
`series/all.parquet`	5,840,115	every headline series: 264,835 series, 5,622 sub-districts, 704 districts, 33 States/UTs
`series/district_extended.parquet`	809,458	24,260 district series of 38 indicators, April 2010 to March 2022 (district sheets FY2010-11 to FY2012-13 plus sub-district sums FY2020-21 to FY2021-22)
`headline_indicators.parquet`	48	headline name, HMIS indicator code and text
`indicators.parquet`	320	every HMIS indicator of the sub-district file with coverage statistics
`legacy_alignment.parquet`	255	how each older district-sheet item maps to a current HMIS indicator
`splits.json`, `tasks.json`		origins, horizons, held-out states; one task per origin × horizon (fev-style)
`forecasts/<model>/<origin>.parquet`		forecasts of the nine leaderboard models, 6 origins each
`leaderboard.json`, `leaderboard.parquet`	1,377	every model × horizon × slice score
`evaluate.py`, `requirements.txt`		the scoring code (numpy + pandas) and its pinned dependencies

Schema

series/bench.parquet and series/all.parquet (one row per series and month):

column	type	meaning
`series_id`	string	`hmis
`ts`	date	month (first day)
`target`	float	reported count, public + private facilities
`state_lgd`, `district_lgd`, `subdistrict`	int	LGD codes of the State/UT, district and sub-district
`state_name`, `district_name`, `subdistrict_name`	string	names
`indicator`, `indicator_name`	string	HMIS indicator code (2017 format) and text
`headline`	string	short name of the headline indicator, e.g. `anc_registered`, `penta3`, `opd_allopathic`
`type`	string	`total`
`cov_public`, `cov_private`, `cov_rural`, `cov_urban`	float	the same count split by facility type and area (covariates)

series/district_extended.parquet: series_id (hmisd|<state_lgd>|<district_lgd>|<indicator>|total), ts, target, state_lgd, district_lgd, indicator, type, origin_src (legacy = FY2010-13 district sheets, subdistrict_sum = FY2020-22 sub-district values summed to the district).

indicators.parquet: indicator, indicator_name, section, n_rows, n_subdistricts, n_months, total, zero_share (share of reported zeros), identity_fail_share (share of cells where public + private differs from rural + urban).

legacy_alignment.parquet: legacy_name (item text in the older sheets), indicator, indicator_name, score (text similarity), method (exact, jaccard or none = not aligned).

Forecast files: series_id, origin, h, ts, q0.1 ... q0.9, mean, target (see above).

Benchmark

Series: 100,000 sub-district × headline-indicator series, drawn in equal numbers from every (headline indicator, State/UT) stratum (smaller strata are taken whole) among the series with at least 18 reported months and a non-zero total.
Origins: 2021-08-01, 2021-09-01, 2021-10-01, 2021-11-01, 2021-12-01, 2022-01-01. Each forecast uses only months before the origin.
Horizons: 1, 2 and 3 months; quantiles 0.1 to 0.9.
Held-out states: Uttar Pradesh (9), Bihar (10), Odisha (21) and Andhra Pradesh (28): 18,555 of the 100,000 series. Slice heldout = these states, indist = the rest.
Metrics: WQL (weighted quantile loss, pooled over a task), MASE (seasonal period 12, falling back to lag 1 when no lag-12 pair exists in the context), cov_80 (empirical coverage of the 0.1-0.9 interval) and win rate against zero-shot Chronos-2 (share of windows with lower MASE). Scores are averaged over the 6 origins, on the 599,714 windows that every model forecast.

Results

All series (lower WQL and MASE are better; a calibrated 80% interval covers 0.80):

h	model	WQL	MASE	cov_80	win rate vs chronos2	windows
1	`tirex`	0.1435	0.8348	0.8278	0.5175	585,323
1	`chronos2`	0.1458	0.8380	0.8267	–	585,323
1	`timesfm25`	0.1459	0.8378	0.7568	0.5193	585,323
1	`lightgbm`	0.1473	0.8349	0.7989	0.5070	585,323
1	`naive`	0.1540	0.9081	0.7441	0.4833	585,323
1	`chronos2_ft_hmis_s0`	0.1553	0.8224	0.8019	0.4985	585,323
1	`chronos2_ft_hmis_s1`	0.1565	0.8249	0.8191	0.4954	585,323
1	`ets_lite`	0.1593	0.8854	0.6756	0.4603	585,323
1	`seasonal_naive`	0.3161	1.2229	0.5726	0.3744	585,323
2	`lightgbm`	0.1574	0.8917	0.7959	0.5050	587,203
2	`tirex`	0.1575	0.8893	0.8404	0.5222	587,203
2	`timesfm25`	0.1582	0.8944	0.7521	0.5217	587,203
2	`chronos2`	0.1594	0.8949	0.8363	–	587,203
2	`chronos2_ft_hmis_s0`	0.1671	0.8738	0.8013	0.5073	587,203
2	`chronos2_ft_hmis_s1`	0.1682	0.8760	0.8184	0.5028	587,203
2	`naive`	0.1709	0.9823	0.7683	0.4674	587,203
2	`ets_lite`	0.1775	0.9563	0.7102	0.4439	587,203
2	`seasonal_naive`	0.3073	1.2408	0.5766	0.3490	587,203
3	`lightgbm`	0.1634	0.9326	0.7923	0.5099	587,740
3	`timesfm25`	0.1662	0.9359	0.7472	0.5247	587,740
3	`tirex`	0.1665	0.9291	0.8476	0.5305	587,740
3	`chronos2`	0.1683	0.9376	0.8409	–	587,740
3	`chronos2_ft_hmis_s0`	0.1770	0.9153	0.8022	0.5094	587,740
3	`chronos2_ft_hmis_s1`	0.1784	0.9177	0.8161	0.5048	587,740
3	`naive`	0.1821	1.0405	0.7854	0.4570	587,740
3	`ets_lite`	0.1897	1.0110	0.7333	0.4346	587,740
3	`seasonal_naive`	0.2997	1.2552	0.5786	0.3425	587,740

Held-out states:

h	model	WQL	MASE	cov_80	win rate vs chronos2	windows
1	`tirex`	0.1662	0.8465	0.8343	0.5269	107,543
1	`timesfm25`	0.1669	0.8513	0.7692	0.5220	107,543
1	`chronos2`	0.1698	0.8530	0.8335	–	107,543
1	`chronos2_ft_hmis_s0`	0.1740	0.8543	0.8112	0.4782	107,543
1	`lightgbm`	0.1772	0.8560	0.8052	0.4986	107,543
2	`timesfm25`	0.1805	0.9203	0.7603	0.5284	107,873
2	`tirex`	0.1826	0.9173	0.8438	0.5317	107,873
2	`chronos2`	0.1862	0.9252	0.8395	–	107,873
2	`chronos2_ft_hmis_s0`	0.1888	0.9248	0.8069	0.4816	107,873
2	`lightgbm`	0.1888	0.9228	0.7983	0.5041	107,873
3	`timesfm25`	0.1929	0.9747	0.7516	0.5352	108,054
3	`tirex`	0.1968	0.9731	0.8485	0.5402	108,054
3	`lightgbm`	0.1984	0.9737	0.7911	0.5147	108,054
3	`chronos2`	0.2006	0.9838	0.8410	–	108,054
3	`chronos2_ft_hmis_s0`	0.2048	0.9848	0.8045	0.4804	108,054

Per-indicator slices (headline=<name>) are in leaderboard.json and the leaderboard Space.

Models:

chronos2: amazon/chronos-2, zero-shot, each series alone.
chronos2_ft_hmis_s0: HMIS-Chronos, Chronos-2 fine-tuned on HMIS-Bench months before August 2021 (held-out states excluded), forecasting all indicators of a sub-district together; chronos2_ft_hmis_s1 is the same model trained with another seed.
timesfm25: google/timesfm-2.5-200m-pytorch, zero-shot.
tirex: NX-AI/TiRex, zero-shot.
lightgbm: one global LightGBM model on lagged values, trained on months before August 2021 without the held-out states.
naive (last value), seasonal_naive (same month a year earlier) and ets_lite (damped trend exponential smoothing), with quantiles from each series' own past errors.

Coverage

5,622 sub-districts in 704 districts of 33 States/UTs; 24 months, April 2020 to March 2022 (FY2020-21 and FY2021-22, the COVID-19 years); 48 headline indicators out of the 320 HMIS indicators in the source file. The district extension adds 38 indicators for FY2010-11 to FY2012-13 where the older district sheets name the same item.

Source data

Derived from the HMIS sub-district monthly reports (indicator-wise, FY2020-21 and FY2021-22) of the Ministry of Health and Family Welfare, published on the Open Government Data Platform India (data.gov.in), snapshot May 2026, under the Government Open Data License - India. The district extension uses the HMIS district-wise (DICR) sheets for FY2010-11 to FY2012-13 from the same Ministry.

Providers: Ministry of Health and Family Welfare (HMIS).

Original data: published by Ministry of Health and Family Welfare (Health Management Information System, HMIS) on the Open Government Data Platform India (data.gov.in) and, where marked, NITI Aayog's National Data and Analytics Platform (NDAP), under the Government Open Data License - India (GODL-India). The original public data remains under GODL-India; the CC BY-NC-SA 4.0 licence of this repository covers JudicialMind's compilation, derived fields, annotations, benchmarks and models.

Citation

@misc{judicialmind2026hmisbench,
  title={HMIS-Bench: Probabilistic Forecasting of Indian Sub-district Health Service Series},
  author={JudicialMind},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/judicialmind/HMIS-Bench}}
}

License

CC BY-NC-SA 4.0 (see LICENSE): research, academic, personal and other non-commercial use, with attribution to JudicialMind and to the original data publishers; adaptations must be shared under the same licence. Commercial use requires a separate licence.

Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.

Downloads last month: 23

Total file size:

883 MB

Models trained or fine-tuned on judicialmind/HMIS-Bench

judicialmind/HMIS-Chronos

Time Series Forecasting • 0.1B • Updated 3 days ago

Space using judicialmind/HMIS-Bench 1

Collection including judicialmind/HMIS-Bench

India Open Government Data — Bharat Health Benchmarks

Collection

Research and non-commercial use (CC BY-NC-SA 4.0). Commercial: surya@judicialmind.ai / hello@judicialmind.ai • 4 items • Updated 3 days ago