Yug

Yug is a 271.8M-parameter time series foundation model developed by Birla AI Labs for zero-shot probabilistic forecasting. A single checkpoint forecasts a previously unseen series without task-specific training or per-series tuning: given a historical context and a forecast horizon, it returns a predictive distribution summarised by quantile levels. It supports univariate and covariate-informed forecasting within a single architecture.

Links

• 💻 GitHub
• 📓 Quickstart notebook
• 🐛 Issues and questions

Overview

Model architecture

Running the model locally

Install the package:

pip install yug

Optional extras:

pip install 'yug[pandas]'      # needed for forecast.to_dataframe()
pip install 'yug[gluonts]'     # GluonTS predictor for benchmark harnesses

Check the machine can run it before downloading weights:

yug-check

Make zero-shot predictions:

import numpy as np
from yug import YugPipeline

pipeline = YugPipeline.from_pretrained(
    "birlaailabs/yug", device_map="cuda"
)

# Synthetic signal: trend + 64-step season + 12-step season + noise
CONTEXT, HORIZON, PERIOD = 2000, 256, 64
rng = np.random.default_rng([20240902, 315595964, 0])
t = np.arange(CONTEXT + HORIZON)
trend = np.linspace(0.0, rng.uniform(50, 150), len(t))
phase1, amp1 = rng.uniform(0, 6), rng.uniform(10, 30)
phase2, amp2 = rng.uniform(0, 6), rng.uniform(2, 8)
signal = (
    trend                                                 # trend
    + amp1 * np.sin(2 * np.pi * t / PERIOD + phase1)      # 64-step season
    + amp2 * np.sin(2 * np.pi * t / 12 + phase2)          # 12-step season
    + rng.normal(0, 1.0, len(t))                          # noise
).astype(np.float32)

context, truth = signal[:CONTEXT], signal[CONTEXT:]

forecast = pipeline.predict(
    context,
    prediction_length=HORIZON,
    freq="D",            # a model input, not metadata: pandas offset alias
    seed=0,              # makes the call reproducible
)

print(forecast.median)                # (256,)     point forecast
print(forecast.quantile(0.9))         # (256,)
print(forecast.interval())            # {"lower": (256,), "upper": (256,)}
print(forecast.to_dataframe().head())

Several series at once (lengths and frequencies may differ):

forecast = pipeline.predict(
    [series_a, series_b, series_c],
    prediction_length=48,
    freq=["D", "D", "H"],
    item_ids=["a", "b", "c"],
)
forecast.values          # (3, n_quantiles, 48)

With covariates — stack channels into a 2-D array, target first:

multivariate = np.stack([target, covariate_1, covariate_2])
forecast = pipeline.predict([multivariate], prediction_length=48)

Covariate forecasts degrade faster with horizon than univariate ones. For long horizons the univariate path is often the better answer — measure both.

In a GluonTS benchmark:

from yug.gluonts_adapter import YugPredictor

predictor = YugPredictor.from_pretrained(
    "birlaailabs/yug", freq="H", prediction_length=48
)
forecasts = list(predictor.predict(dataset))

Training data

• Subset of Chronos Datasets
• Subset of GIFT-Eval Pretrain
• Proprietary synthetic data

Citation

If you find Yug useful for your research, please consider citing it:

@software{yug_2026,
  title  = {Yug: a foundation model for zero-shot probabilistic time-series forecasting},
  author = {Aaditya Jain* and Debdeep Sanyal* and Aaryan Nagpal and Dhruv Kumar and Murari Mandal and Saurabh Deshpande},
  year   = {2026},
  organization = {Birla AI Labs},
  url    = {https://github.com/birla-ai-labs/yug}
}

*Equal contribution.

Acknowledgements

We thank Karthik Sridhar, Deepak Lenka, Akash Kokare, Manya Pandey, and Varya Srivastava, as well as the extended Birla AI Labs team for their support.

License

Model weights : CC-BY-NC 4.0

Code : licensed separately — see GitHub repository for its license.