let's do it like they did in 1924 and draw forecasts by hand
2026-10-29

Ensembles Are Called Agents Now

The GIFT-eval forecast benchmark allows submissions to be listed under model type “agentic”. From its README:

agentic: Multi-step systems that use agents or LLMs to reason, generate or select forecasts.

Its leaderboard is now dominated by models tagged as such (indicated by the yellow circle emoji). Not being able to check all of the submissions (as some did not even provide links to documentation let alone replication code), at least a good chunk of the top submissions are barely using “reasoning”. Instead, they are ensembles, combining models such as TimesFM, Chronos, Toto, and TiRex.

TW3Cast is a complicated ensemble. From its README:

Listed in the leaderboard’s multi-model (agentic) category, but it runs no agent and no LLM, and makes no inference-time decisions: a frozen per-configuration router over specialized models.

While there is a bit more to it, TW3Cast is a uniform ensemble for some datasets, as listed in its router.csv file. For example, for the Car Parts dataset, it uses a uniform ensemble of Chronos-2, TimesFM, and TiRex-2.

PRISM by Birla AI Labs is an ensemble:

Prism is a leakage-free probabilistic forecasting ensemble developed by the Frontier Research and DeepTech Team at Birla AI Labs. Rather than training a new model, Prism combines the quantile outputs of several pretrained foundation models, learning how to blend them from a held-out validation window that never touches test data. The key insight is that different models have complementary strengths: learning a separate blend at each quantile level means the model best at the median does not have to be the model that covers the tails.

RacineCast-1 and RacineCast-1-NC (i.e. non-commercial) are weighted ensembles of models. RacineAI summarizes their approach in this recipe:

0.55 · Toto-2.0-FnF + 0.16 · Toto-2.0-2.5B + 0.16 · TiRex-2 + 0.045 · Chronos-2 + 0.045 · FlowState-r1.1 + 0.03 · PatchTST-FM-r1

Back in July of this year, it ranked them first on the leaderboard.

55% of the RacineCast approach is Toto-2.0-FnF (as in Family-and-Friends). Toto-2.0-FnF is an ensemble of different Toto checkpoints and other models—meta-learned by a FFORMA-style XGBoost. It, too, is submitted as “agentic” to the GIFT-eval leaderboard.

My personal favorite, however, is IK-QuadTSFM. It is a uniform ensemble of Chronos-2, TimesFM-3, TiRex-2, and Toto-2.0-FT, and as such questions the additional complexity of any other “agentic” submission in its vicinity.

Screenshot of GIFT-eval leaderboard.
Right-click to open the full-size image. Screenshot of the GIFT-eval overall leaderboard ranked by the default MASE_Rank. The yellow emoji stands for GIFT-eval’s “agentic” model type, red stands for “zero-shot”, green for “pretrained”.

The above screenshot was taken on October 29, 2026. It shows the default sorting based on the MASE_Rank column. Sorting by MASE would shuffle the results somewhat, though “Stride w/ Synapse” and “EXAONE-Forecast-Agent” would continue to rank at the top. The rest would cover the range from 0.650 MASE (for LS-MoE and TIMEHEDGE) over 0.663 for the aforementioned IK-QuadTSFM and 0.667 for TimesFM, to 0.698 for Chronos-2.