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Mandi-Chronos

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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Mandi-Chronos is Chronos-2 fine-tuned to forecast daily wholesale (mandi) prices in India. Given a market's daily modal price history for one commodity variety (Rs per quintal, with gaps on days without trade), it returns probabilistic forecasts (quantiles) for the next days. It was fine-tuned on the MandiBench series before 2025-06-01, leaving out the nine benchmark held-out states and union territories, and on MandiBench it beats zero-shot Chronos-2 at every horizon, both overall and in the held-out states. Architecture and API are those of Chronos-2 (about 120M parameters).

Quickstart

pip install -r requirements.txt   # chronos-forecasting, torch, transformers, pandas, ...

import pandas as pd
import torch
from chronos import Chronos2Pipeline
from huggingface_hub import hf_hub_download

repo = "judicialmind/Mandi-Chronos"
pipe = Chronos2Pipeline.from_pretrained(repo, device_map="cpu")  # device_map="cuda" on a GPU

# daily modal prices (Rs per quintal) of three Agmarknet series over the 512 days before 2025-06-01
df = pd.read_csv(hf_hub_download(repo, "example.csv"), parse_dates=["ts"])
origin = pd.Timestamp("2025-06-01")
days = pd.date_range(origin - pd.Timedelta(days=512), periods=512, freq="D")
ids, contexts = [], []
for sid, g in df.groupby("series_id"):
    ids.append(sid)
    contexts.append(torch.tensor(g.set_index("ts")["target"].reindex(days).to_numpy(), dtype=torch.float32))

quantiles, mean = pipe.predict_quantiles(contexts, prediction_length=30, quantile_levels=[0.1, 0.5, 0.9])
for sid, q in zip(ids, quantiles):  # q: (1 variate, 30 days, 3 quantiles)
    print(" | ".join(sid.split("|")[1:5]))
    print("  day 1 :", [round(v) for v in q[0, 0].tolist()], " day 30:", [round(v) for v in q[0, -1].tolist()])

Forecast quantiles, Rs per quintal (CPU, float32)

state	district	market	commodity	day 1 q0.1	day 1 median	day 1 q0.9	day 30 q0.1	day 30 median	day 30 q0.9
Odisha	Dhenkanal	Hindol	Tomato	2,496	2,798	3,116	2,440	3,491	5,174
Uttar Pradesh	Prayagraj	Ajuha	Onion	1,163	1,208	1,262	1,496	1,805	2,121
Uttar Pradesh	Raebarelli	Raibareilly	Wheat	2,425	2,436	2,445	2,392	2,458	2,516

Input: one 1-D float tensor per series, the daily price on a calendar-day grid ending the day before the first forecast day, with NaN for days without trade. The MandiBench protocol uses the last 512 days. Output: predict_quantiles returns, per series, a tensor of shape (1, prediction_length, len(quantile_levels)) in the input's unit, and the mean forecast. pipe.predict_df(df, prediction_length=30, id_column=..., timestamp_column=..., target=...) takes a long pandas table instead.

Command line

mandi_chronos.py builds the daily contexts from a long table (series_id, ts, target, csv or parquet) and writes forecasts in the MandiBench submission format (series_id, origin, h, ts, q0.1 ... q0.9):

python mandi_chronos.py example.csv --origin 2025-06-01 --out forecasts.csv
# 3 series x 30 days -> forecasts.csv

In Python: from mandi_chronos import load, forecast; forecast(load(), df, "2025-06-01"). On a GPU it loads in bfloat16 (the precision used for the benchmark forecasts); float32 on CPU agrees with them to about 0.1% (median relative difference of the quantiles).

Results

MandiBench test windows: 11 monthly origins from 2025-06-01 to 2026-04-01, horizons of 7, 14 and 30 days. WQL (weighted quantile loss): lower is better; dir_acc_7d: share of windows where the median forecast moves in the same direction as the price within the first week. Mandi-Chronos is chronos2_ft_a in the MandiBench files.

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	dir_acc_7d
Mandi-Chronos	0.0665	0.0778	0.0990	0.6271
Chronos-2 (zero-shot)	0.0677	0.0799	0.1025	0.6156
TimesFM 2.5 (zero-shot)	0.0672	0.0793	0.1012	0.6109
TiRex (zero-shot)	0.0709	0.0827	0.1038	0.6204
LightGBM (global)	0.0700	0.0831	0.1087	0.6025
ETS (damped trend)	0.0708	0.0862	0.1153	0.6109
Seasonal naive (7 days)	0.0845	0.0957	0.1193	0.1037

Held-out states (Chandigarh, Himachal Pradesh, Madhya Pradesh, Manipur, Meghalaya, Odisha, Rajasthan, Tamil Nadu, West Bengal; not seen in fine-tuning; windows: 13,176 / 13,875 / 14,479)

model	WQL h7	WQL h14	WQL h30	dir_acc_7d
Mandi-Chronos	0.0532	0.0641	0.0847	0.6150
Chronos-2 (zero-shot)	0.0541	0.0652	0.0858	0.6008
TimesFM 2.5 (zero-shot)	0.0541	0.0653	0.0869	0.5955
TiRex (zero-shot)	0.0559	0.0665	0.0863	0.6137
LightGBM (global)	0.0551	0.0666	0.0894	0.6002
ETS (damped trend)	0.0569	0.0703	0.0947	0.6014
Seasonal naive (7 days)	0.0682	0.0783	0.0990	0.1106

Bold: lowest WQL in the table. Mandi-Chronos also has the highest dir_acc_7d in both tables. It needs only the price history. The full leaderboard (MASE and interval coverage included), all forecasts and the scoring script are in the MandiBench dataset.

Files

file	content
`model.safetensors`, `config.json`	Chronos-2 weights and configuration (float32)
`mandi_chronos.py`	`load`, `daily_contexts`, `predict`, `forecast` and the command line above
`example.csv`	three MandiBench series (`series_id, ts, target`), 2024-01-06 to 2025-05-31
`requirements.txt`	pinned runtime dependencies

Coverage

Fine-tuned and evaluated on 12,863 MandiBench series: daily modal prices of 176 food commodities in 2,126 Agmarknet markets in 24 states and union territories. The fine-tuning data ends before 2025-06-01; the test windows run from 2025-06-01 to 2026-04-30.

Source data

Training and example data: 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.

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{judicialmind2026mandichronos,
  title        = {Mandi-Chronos: Chronos-2 fine-tuned for daily mandi price forecasting in India},
  author       = {JudicialMind},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/judicialmind/Mandi-Chronos}}
}

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.

Base model(s): amazon/chronos-2 (apache-2.0). Their licence terms and notices continue to apply to the upstream components; see THIRD_PARTY_NOTICES.md.