| --- |
| 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} |
| } |
| ``` |
|
|
