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.

MandiBench

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

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MandiBench is a probabilistic forecasting benchmark for daily wholesale (mandi) prices in India. Each of its 12,863 series is the daily modal price of one commodity variety in one Agmarknet market (Rs per quintal), with history from 2001 to May 2026. Forecasters predict the next 7, 14 and 30 days from 11 monthly origins (2025-06-01 to 2026-04-01), as nine quantiles (q0.1 ... q0.9). Nine state/UT groups are held out entirely, to test transfer to markets a trained model has never seen. The repository ships the series, the forecasts of nine models, the leaderboard and evaluate.py, which recomputes the leaderboard from the shipped forecasts and scores new models.

Headline result: our Chronos-2 fine-tunes top the leaderboard at every horizon, on all series and in the held-out states: the group-context fine-tune reaches WQL 0.0930 at 30 days (0.0796 held-out) and the released Mandi-Chronos 0.0990 (0.0847), against 0.1012 for TimesFM-2.5 and 0.1025 for zero-shot Chronos-2.

Quickstart

from huggingface_hub import snapshot_download

root = snapshot_download("judicialmind/MandiBench", repo_type="dataset")

import pandas as pd

series = pd.read_parquet(f"{root}/series/all.parquet")
print(series.shape)
print(series[["series_id", "ts", "target", "cclass"]].head(3).to_string())

Output: (27437227, 14) (27,437,227 rows, 14 columns), then:

First three rows of series/all.parquet

series id	date	target	commodity class
mandi\|Andhra Pradesh\|Anantapur\|Anantapur\|Mousambi(Sweet Lime)\|Mousambi	2014-05-27	1,600	fruit
mandi\|Andhra Pradesh\|Anantapur\|Anantapur\|Mousambi(Sweet Lime)\|Mousambi	2014-06-07	1,570	fruit
mandi\|Andhra Pradesh\|Anantapur\|Anantapur\|Mousambi(Sweet Lime)\|Mousambi	2014-06-20	1,400	fruit

Other loaders: datasets.load_dataset("judicialmind/MandiBench", "series", split="train"), or DuckDB (SELECT commodity, avg(target) FROM 'series/all.parquet' GROUP BY 1).

Evaluate a model

evaluate.py (numpy, pandas, pyarrow; pip install -r requirements.txt) exposes the benchmark windows and their contexts as arrays. Row i of every array is window bench.windows.iloc[i] (series_id, origin, state, cclass, heldout). Input: bench.context (windows x 512) holds the daily prices of the 512 days before the origin (NaN = no trade that day). Output: an array of shape (windows, 30, 9), the forecast for days origin ... origin + 29 at the nine quantile levels in QUANTILES.

import sys

import numpy as np
from statistics import NormalDist

sys.path.insert(0, root)
from evaluate import QUANTILES, Benchmark

bench = Benchmark(root)
print(len(bench.windows), "windows")

# toy forecaster: last observed price, normal spread of 5% x sqrt(h)
z = np.array([NormalDist().inv_cdf(q) for q in QUANTILES])
h = np.arange(1, 31)[:, None]
Q = bench.last[:, None, None] * (1 + 0.05 * np.sqrt(h) * z)   # (windows, 30 days, 9 quantiles)

res = bench.score(Q, "last_value")
print(res[res["slice"].isin(["all", "heldout"])][["h", "slice", "wql", "mase", "cov_80"]].round(4).to_string(index=False))

Output: 96648 windows (96,648 benchmark windows), then:

Toy forecaster last_value, by horizon and slice

horizon (days)	slice	WQL	MASE	80% coverage
7	all	0.0791	1.1059	0.7254
7	heldout	0.0649	1.0358	0.7948
14	all	0.0936	1.4293	0.7511
14	heldout	0.0787	1.2900	0.8131
30	all	0.1204	1.8335	0.7807
30	heldout	0.1036	1.6976	0.8343

From the command line (run inside the downloaded folder):

python evaluate.py                                    # recompute the leaderboard from forecasts/ -> "REPRODUCED"
python evaluate.py --write-windows windows.csv        # the 96,648 (series_id, origin) windows to forecast
python evaluate.py --submission my_forecasts.parquet --name my_model --out my_model.csv

A submission file (or a folder of parquet/csv files) has one row per window and day: series_id, origin, h, q0.1, q0.2, ..., q0.9, with origin the first forecast day (ISO date) and h = 1 ... 30 (day origin + h - 1). Use only observations dated before origin. The output adds win_rate, the share of windows where the model's MASE beats chronos2. To appear on the MandiBench-Leaderboard, open a discussion on this repository with the --out csv and a short model description.

Tasks and metrics

Origins: the first day of each month from 2025-06-01 to 2026-04-01 (11). The context is every observation before the origin; a model may use the full history (the shipped models read at most the last 512 days).
Horizons: 7, 14 and 30 days. One call per (series, origin) forecasts 30 days; a horizon-h task scores days 1 ... h. Only days with an observed price count.
Held-out states: Chandigarh, Himachal Pradesh, Madhya Pradesh, Manipur, Meghalaya, Odisha, Rajasthan, Tamil Nadu, West Bengal (splits.json). The trained models (LightGBM and the fine-tuned Chronos-2 variants) used only data before 2025-06-01 from the other states. Slices: all, heldout, indist, and one per commodity class (cclass=cereal, ...).
Metrics (computed per origin x horizon, then averaged over origins):
- wql: weighted quantile loss, the mean over the 9 quantiles of 2 x summed pinball loss / summed |y|, pooled over the windows of the task. Lower is better.
- mase: mean absolute error of q0.5 divided by the mean absolute 7-day change of the 512-day context (lag 1 when no 7-day pair is observed), averaged over windows. Lower is better.
- cov_80: share of observed days inside [q0.1, q0.9]; 0.80 is ideal.
- dir_acc_7d: share of windows where q0.5 moves in the same direction as the price from the last observed value, at the last observed day within the first week (windows with no change are skipped).
- n_windows: windows with a defined MASE. The leaderboard scores the 96,648 (series, origin) windows that all nine models forecast.

Results

Leaderboard, sorted by WQL at 30 days (leaderboard.parquet; per-class slices and win_rate vs chronos2 are in leaderboard.json).

All series (windows: 88,899 at h=7, 92,776 at h=14, 96,341 at h=30)

model	WQL h7	WQL h14	WQL h30	MASE h7	MASE h14	MASE h30	cov_80 h30	dir_acc_7d
chronos2_ft_b	0.0633	0.0736	0.0930	1.0660	1.3494	1.6990	0.7289	0.6729
chronos2_ft_c	0.0656	0.0765	0.0969	1.1077	1.4087	1.7858	0.7678	0.6370
chronos2_ft_a (Mandi-Chronos)	0.0665	0.0778	0.0990	1.1126	1.4177	1.8018	0.7215	0.6271
timesfm25	0.0672	0.0793	0.1012	1.0833	1.4031	1.8046	0.7410	0.6109
chronos2	0.0677	0.0799	0.1025	1.1487	1.4675	1.8759	0.7490	0.6156
tirex	0.0709	0.0827	0.1038	1.2017	1.5219	1.9185	0.7605	0.6204
lightgbm	0.0700	0.0831	0.1087	1.2280	1.6134	2.1358	0.7704	0.6025
ets_lite	0.0708	0.0862	0.1153	1.0865	1.4099	1.8143	0.8532	0.6109
seasonal_naive	0.0845	0.0957	0.1193	1.2682	1.5662	1.9484	0.8003	0.1037

Held-out states (windows: 13,176 at h=7, 13,875 at h=14, 14,479 at h=30)

model	WQL h7	WQL h14	WQL h30	MASE h7	MASE h14	MASE h30	cov_80 h30	dir_acc_7d
chronos2_ft_b	0.0511	0.0609	0.0796	1.0379	1.2741	1.6773	0.7126	0.6531
chronos2_ft_c	0.0523	0.0626	0.0819	1.0734	1.3154	1.7252	0.7608	0.6264
chronos2_ft_a (Mandi-Chronos)	0.0532	0.0641	0.0847	1.0734	1.3243	1.7485	0.7221	0.6150
chronos2	0.0541	0.0652	0.0858	1.1387	1.3944	1.8477	0.7466	0.6008
tirex	0.0559	0.0665	0.0863	1.1729	1.4408	1.8897	0.7618	0.6137
timesfm25	0.0541	0.0653	0.0869	1.0336	1.2881	1.7270	0.7269	0.5955
lightgbm	0.0551	0.0666	0.0894	1.0864	1.3739	1.9001	0.7963	0.6002
ets_lite	0.0569	0.0703	0.0947	1.0352	1.2910	1.7046	0.8391	0.6014
seasonal_naive	0.0682	0.0783	0.0990	1.1941	1.4292	1.8256	0.7936	0.1106

Models:

model	description
`seasonal_naive`	price on the same weekday of the last week; spread from past 7-day changes (its median often equals the last price, which scores as a wrong direction in `dir_acc_7d`)
`ets_lite`	damped additive-trend exponential smoothing per series; spread from one-step residuals
`lightgbm`	global LightGBM quantile regression on scaled lags, rolling statistics and calendar features
`chronos2`	`amazon/chronos-2`, zero-shot
`timesfm25`	`google/timesfm-2.5-200m-pytorch`, zero-shot
`tirex`	`NX-AI/TiRex`, zero-shot
`chronos2_ft_a`	Chronos-2 fine-tuned on MandiBench series, univariate: released as Mandi-Chronos
`chronos2_ft_b`	Chronos-2 fine-tuned and run with group context: the series of one state and commodity are forecast jointly
`chronos2_ft_c`	Chronos-2 fine-tuned with daily soil-moisture and reservoir-storage covariates (NRSC and CWC data from data.gov.in, not included here)

Files

file	content
`series/all.parquet`	the benchmark series, one row per series and trading day (27,437,227 rows)
`series/catalog.parquet`	all 178,352 candidate market x commodity x variety series with coverage counts; `kept` marks the 12,863 benchmark series
`splits.json`	origins, horizons, held-out states, season length (7)
`tasks.json`	the 33 origin x horizon tasks in fev-style task format (id/timestamp/target columns, horizon, quantile levels)
`forecasts/<model>/<origin>.parquet`	the forecasts of the nine models, one file per origin
`leaderboard.parquet`, `leaderboard.json`	the leaderboard (324 rows: model x horizon x slice); the json adds `win_rate`
`evaluate.py`, `requirements.txt`	evaluation harness

Schema

series/all.parquet

column	type	description
`series_id`	string	`mandi
`ts`	date	trading day
`target`	float64	modal price, Rs per quintal (median over the grades reported that day)
`state`, `district`, `market`	string	Agmarknet market location
`commodity`, `variety`	string	Agmarknet commodity and variety names
`cclass`	string	commodity class: cereal, pulse, oilseed, vegetable, fruit, spice, sugar, dairy_meat_fish, top (tomato, onion, potato)
`grade`	string	always `NA` (grades are merged)
`district_lgd`	int32	empty in this release
`cov_min_price`, `cov_max_price`	float64	minimum and maximum price that day (median over grades), Rs per quintal
`n_grades`	int64	number of grade rows merged into the day

series/catalog.parquet: state, district, market, commodity, variety, cclass, first_ts/last_ts (first and last trading day), n_obs_pre / first_pre (days and first day before 2025-06-01), n_obs_post (days from 2025-06-01), n_mkt_days (trading days of the market from first_pre to 2025-06-01), n_obs_recent / mkt_days_recent (days of the series and of its market in the two years before 2025-06-01), obs_ratio (= n_obs_recent / mkt_days_recent), series_id, kept.

forecasts/<model>/<origin>.parquet: series_id, origin, h, ts, q0.1 ... q0.9, mean, target (one row per window and forecast day; target = observed price, null when the market did not trade).

Coverage

12,863 series from 2,126 markets in 457 districts of 24 states and union territories, 176 food commodities in nine classes (vegetables 4,622 series, tomato/onion/potato 2,030, cereals 1,742, fruit 1,510, spices 1,285, oilseeds 798, pulses 725, sugar 129, dairy/meat/fish 22). Daily prices from 2001-01-10 to 2026-05-15; 25,729,325 rows before the first origin and 1,707,902 from it. A series is included when it has at least two years of history before 2025-06-01 and traded on at least half of its market's trading days in the two years before that date.

The prices come from 83.2 million Agmarknet rows. 14.0 million exact paging duplicates were removed, as were non-food commodities (4.6 million rows) and 90,130 flagged rows (unit slips, outliers and non-positive prices).

Source data

Derived from 1 resource published on the Open Government Data Platform India (data.gov.in), snapshot May 2026, under the Government Open Data License - India.

Providers: Directorate of Marketing and Inspection (Agmarknet), Ministry of Agriculture and Farmers Welfare.

Directorate of Marketing and Inspection (Agmarknet), Ministry of Agriculture and Farmers Welfare. "Variety-wise Daily Market Prices Data of Commodity". data.gov.in resource 35985678-0d79-46b4-9ed6-6f13308a1d24, https://api.data.gov.in/resource/35985678-0d79-46b4-9ed6-6f13308a1d24. Snapshot: May 2026.

GODL-India does not cover personal information; this dataset holds market prices only.

Original data: published by Directorate of Marketing and Inspection (Agmarknet), Ministry of Agriculture and Farmers Welfare 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{judicialmind2026mandibench,
  title        = {MandiBench: a probabilistic forecasting benchmark for daily mandi prices in India},
  author       = {JudicialMind},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/judicialmind/MandiBench}}
}

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.

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