---
license: other
tags:
  - time-series
  - forecasting
  - foundation-model
  - zero-shot
  - gift-eval
pipeline_tag: time-series-forecasting
library_name: pytorch
---

# 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](https://huggingface.co/spaces/Salesforce/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}
}
```