---
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.