| --- |
| pretty_name: RealCast-bench Leaderboard |
| tags: |
| - time-series |
| - forecasting |
| - leaderboard |
| - foundation-models |
| configs: |
| - config_name: leaderboard |
| data_files: leaderboard.csv |
| default: true |
| - config_name: tasks |
| data_files: tasks.csv |
| - config_name: pairwise |
| data_files: pairwise.csv |
| --- |
| |
| # RealCast-bench Leaderboard |
|
|
| Time series foundation models evaluated zero-shot on proprietary Carbon Arc |
| commercial panels: 10 source panels, 81 datasets, 290 tasks across three boards |
| (152 weekly, 102 monthly, 36 daily). Test year: October 2025 to September 2026. |
| This table mirrors Table 2 of the paper *RealCast-bench: Evaluating Time Series Foundation Models on Proprietary Commercial Panels*. The panels themselves are |
| proprietary and are not released. |
|
|
| ## Columns |
|
|
| - **`*_rel_mase`**: geometric mean over tasks of each task's MASE divided by |
| Seasonal Naive's MASE. Lower is better; Seasonal Naive = 1.000. `overall` pools |
| all 290 tasks, and the board columns score each board on its own. Ratios are not |
| comparable across boards: daily Seasonal Naive repeats last week's value, a much |
| stronger reference than weekly and monthly Seasonal Naive, which repeat the value |
| from a year earlier. |
| - **`*_rank`**: rank among the 21 configurations. |
| - **`win_rate`**: share of (task, other configuration) pairings in which this |
| configuration has the lower MASE, with ties counted as half. |
| - **`license`**, **`license_url`**: the license of the released weights, with a link to |
| its text. |
| - **`commercial_use`**: `yes` (Apache-2.0), `no` (non-commercial license) or |
| `restricted` (commercial use permitted under conditions; see the license). |
| - **`covariates`**: `none` means the model saw only the target series. `past + |
| calendar` means it also saw the panel's other measured series up to the forecast |
| origin, plus calendar features. |
|
|
| ## Per-task results (`tasks` config) |
|
|
| One row per (board, dataset, horizon, configuration): 290 tasks × 21 |
| configurations. A dataset is one panel at one entity level (`original`, |
| `company`, `product`, `service`, `retailer`). `rel_mase` and `rel_wql` are the |
| task's MASE and WQL divided by Seasonal Naive's; their geometric mean over tasks |
| gives the leaderboard scores. `coverage_80pct` is the share of actuals inside the |
| 10–90% interval. Receipt Spend is scored separately in the paper and is not |
| included. |
|
|
| AutoETS's WQL explodes on 11 tasks (up to about 1e35, worst on daily |
| `app_downloads` at 91 days): on long horizons a few windows produce runaway |
| prediction intervals. These values are reported as computed. The leaderboard |
| ranks by MASE, which they do not affect. |
|
|
| ## Pairwise comparison (`pairwise` config) |
|
|
| Pairwise win rate and skill score between configurations, computed from `tasks.csv` with |
| [fev-bench](https://github.com/autogluon/fev)'s method (`fev.analysis.pairwise_comparison`, |
| commit f9e2c7d, reimplemented; it reproduces fev's output exactly). One row per (board, metric, model_1, model_2); `board` is `all`, `weekly`, |
| `monthly` or `daily`, and `metric` is `mase` or `wql`. `win_rate` is the share of tasks on |
| which `model_1` has the lower error than `model_2`, ties counted as half. `skill_score` is 1 |
| minus the geometric mean over tasks of `model_1`'s error divided by `model_2`'s, with ratios |
| clipped to [0.01, 100]. `_lower` and `_upper` bound the 95% interval from 1,000 bootstrap |
| resamples of tasks (seed 123). As on fev-bench, each (board, metric) holds the 15 |
| configurations with the highest average win rate, plus Seasonal Naive. `+ cov.` means past + |
| calendar covariates. |
|
|
| ## Setup |
|
|
| - No model is fine-tuned on Carbon Arc data. |
| - Symmetric averaging (averaging the forecast of a series with the sign-restored forecast |
| of its negation) is enabled for every model that offers it: TimesFM-2.5, TimesFM-3.0, |
| Timestral-209M and the three t0 models. Other settings are each library's defaults. |
|
|
| ## Submitting a model |
|
|
| Point us to the model's public weights and forecasting package. We run it through |
| the same wrapper, entities and windows as every other configuration and add it to |
| all three boards. |
|
|
