CHARM

CHARM is a zero-shot probabilistic time-series foundation model from C3 AI. It produces full quantile forecasts (99 quantile levels) for arbitrary horizons and is evaluated zero-shot on the GIFT-Eval benchmark — no GIFT-Eval data is used in training.

Availability: CHARM is currently closed-source. Model weights and inference/replication code are not publicly released at this time. This page serves as the model card and benchmark record.

Model summary

Parameters	~63.3M (encoder ~59M)
Hidden size (d_model)	384
Projection	TCN-pool (patch size 16, causal conv stack + residual gate)
Backbone	Transformer encoder with RoPE attention
Decoder	Quantile decoder, 99 levels (0.01–0.99)
Max context length	8192
Output	Probabilistic (quantile) forecasts, multivariate-capable
Precision	float32

Intended use

Zero-shot probabilistic forecasting of univariate and multivariate time series across domains (energy, transport, sales, healthcare, nature, web/cloud-ops, econ/finance). The model is applied without any per-dataset fine-tuning.

GIFT-Eval results

Evaluated zero-shot on the full GIFT-Eval benchmark (97 dataset/frequency/term configurations) using the standard 11-metric protocol. Aggregate scores (geometric mean of per-config metrics normalized to the Seasonal Naive baseline; lower is better):

Metric	Score (rel. Seasonal Naive)
MASE	0.7582
CRPS (mean weighted sum quantile loss)	0.4776

Per-term (geometric mean, normalized to Seasonal Naive):

Term	MASE	CRPS
short	0.7463	0.5036
medium	0.7577	0.4452
long	0.7911	0.4460

Scores are the geometric mean of per-config metric / Seasonal Naive across all 97 GIFT-Eval configurations (lower is better; < 1.0 beats Seasonal Naive). Full per-config results are in all_results.csv.

Evaluation protocol

• Benchmark: GIFT-Eval, 97 configs (short / medium / long terms).
• Metrics: MSE[mean], MSE[0.5], MAE[0.5], MASE[0.5], MAPE[0.5], sMAPE[0.5], MSIS, RMSE[mean], NRMSE[mean], ND[0.5], mean_weighted_sum_quantile_loss (computed with gluonts evaluate_forecasts).
• Context length: 8192; forecasts are full quantile distributions.
• Zero-shot: no GIFT-Eval train/test data is seen during pretraining (testdata_leakage = No).

Limitations

• Forecast quality varies by domain and horizon; very long horizons and highly non-stationary series remain challenging.
• Quantile calibration is learned and may drift on out-of-distribution scales.

Citation

@misc{charm,
  title  = {CHARM: A Zero-Shot Time-Series Foundation Model},
  author = {C3 AI},
  year   = {2026},
  url    = {https://huggingface.co/c3aiia3c/CHARM}
}