ChakraTS

ChakraTS is a zero-shot probabilistic time-series forecasting model from YHat Labs, served as a hosted API. This repository holds the model card; there are no weights to download.

Send a batch of series and a horizon, get back nine quantiles (10% to 90%) for every future step. ChakraTS forecasts zero-shot: nothing is trained or fitted on your data, and fine-tuning on customer data is not part of this release. It accepts known-future covariates, works at any regular frequency from minutes to years, and returns the per-step prediction intervals that scheduling, procurement, bidding and inventory decisions depend on.

• Website: https://yhatlabs.com
• Request API access: https://yhatlabs.com/access
• Code, benchmark files and notebooks: https://github.com/yhatlabs/yhatlabs
• Leaderboards(coming soon): GIFT-Eval · fev-bench
• Research configuration (non-commercial): yhatlabs/ChakraTS-Lab

Getting started

Access is by API key, issued on request. One call forecasts a batch of series:

curl -X POST https://api.yhatlabs.com/v1/forecast \
  -H "Authorization: Bearer $YHAT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "series": [
          {"id": "store_1", "values": [12, 15, 11, 18, 20, 17, 22, 19, 24, 21, 25, 23, 27, 26]},
          {"id": "store_2", "values": [40, 38, 45, 47, 44, 50, 52, 49, 55, 53, 58, 57, 60, 62]}
        ],
        "horizon": 7,
        "freq": "D"
      }'

Response (abridged):

{
  "horizon": 7,
  "quantile_levels": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
  "forecasts": [
    {"id": "store_1", "quantiles": [[22.1, 23.4, 24.3, 25.0, 25.7, 26.4, 27.2, 28.2, 29.8], "..."]},
    {"id": "store_2", "quantiles": ["..."]}
  ],
  "weights": {"...": "contribution of each underlying forecaster to this request"}
}

A Python client (pip install yhatlabs) with a DataFrame interface, automatic frequency inference and the same argument names as other forecasting APIs is in preparation; it will be published in the GitHub repository.

Input and output contract

Field	Meaning
series[].id	your identifier, echoed back
series[].values	past observations, oldest first, at a regular spacing; null marks a missing value
series[].future_covariates (optional)	known-future inputs aligned to the horizon, for example a demand forecast, a published day-ahead price, a promotion or holiday flag
horizon	number of steps to forecast
freq	spacing of the observations as a pandas offset alias (5min, 15min, h, D, W, MS, QS, YS)
forecasts[].quantiles	a [horizon, 9] array for the levels 0.1, 0.2, …, 0.9
weights	how much each underlying forecaster contributed to this request

Quantiles are monotone in the level. The median (level 0.5) is the point forecast.

Capabilities

Property	Detail
Training on your data	none; zero-shot
Context	up to 16,384 past points per series
Horizon	up to 1,024 steps
Frequencies	any regular frequency, 5-minute to yearly
Covariates	known-future numeric or categorical inputs
Missing values	allowed, marked as null
Output	9 quantiles per step (10% to 90%), monotone
Batching	many series per request; latency grows with the batch, not with history length

Evaluation

Leaderboard submissions are not yet published; the numbers below are from our runs of the official protocols.

All numbers come from the official benchmark protocols on the full benchmarks, scored against the official seasonal-naive baselines. No benchmark test data is used at any point, and no benchmark training split is used to fit anything.

GIFT-Eval (97 dataset configurations, 23 datasets) — not yet published

Geometric mean over configurations, relative to seasonal naive; lower is better. Comparison rows are the official leaderboard entries.

Model	MASE	CRPS
ChakraTS	0.671	0.461
Toto-2.0-FnF	0.676	0.463
T0-beta	0.687	0.474
TiRex-2	0.697	0.478
Chronos-2	0.698	0.485
TimesFM-2.5	0.705	0.490

fev-bench (100 tasks) — not yet published

Scaled quantile loss skill score vs seasonal naive; higher is better. The ChakraTS entry is its fev-bench evaluation configuration (chakra-ts-fev), which uses no component pretrained on fev-bench data, so no task is flagged for leakage.

Model	Skill score
ChakraTS	48.1
TimesFM-3 (non-commercial)	48.7
Chronos-2	47.3
TimesFM-2.5	42.2 (leaderboard figure, after its leakage replacement)
T0-beta	46.7
TiRex-2	45.5
Toto-2.0-2.5B	44.4

Per-task result files and the evaluation notebooks are in the GitHub repository.

Approach

ChakraTS is a mixture of expert forecasters. Several pretrained forecasting models each produce a full predictive distribution for every series. Nothing is learned across customers or across requests: the same rule is applied to every series, there is no trained gate that could memorise a benchmark.

YHat Labs has trained its own forecasting models. The current one that powers ChakraTS is a 30M-parameter patch-based transformer trained on a curated corpus that is disjoint from the public benchmarks. It is one of the expert in the served pool.

Intended use

• Energy: load, renewable generation and electricity price forecasting at 5-minute to daily resolution, with grid operators' published forecasts as covariates.
• Retail and supply chain: demand forecasting per product and location, with promotions, prices and calendar effects as covariates; safety-stock and replenishment from the quantiles.
• Operations and infrastructure: traffic, capacity, call volumes, cloud and sensor metrics for planning and alerting.
• Finance and planning: revenue, cash-flow and KPI projections with prediction intervals.
• Scenario analysis: the same series under alternative covariate paths (a different price, a different weather forecast).
• Backtesting: rolling-origin evaluation on your own history before adopting a forecast, using the same endpoint.

Not intended use

• Series without a regular time index.
• Histories shorter than a few seasonal cycles: forecasts are returned, but the intervals will be wide and should be treated accordingly.
• Classification of events or anomaly detection as such; ChakraTS returns forecast distributions, from which such signals can be derived but which it does not label. Check out ChakraTab model for this usecase.

Data handling

Series sent to the API are used only to produce the response. They are not stored beyond the request, not used for training and not shared with third parties. Request metadata (timestamps, sizes, latency) is logged for operations and billing.

Terms

Use of the API is governed by the YHat Labs API terms. ChakraTS is available for commercial use under those terms; enterprise and on-premises arrangements on request.

Citation

@misc{chakrats2026,
  title  = {ChakraTS: zero-shot probabilistic time-series forecasting as a service},
  author = {YHat Labs},
  year   = {2026},
  url    = {https://huggingface.co/yhatlabs/ChakraTS}
}

Contact

founders@yhatlabs.com · https://yhatlabs.com