摘要：TW3Cast 是一套时间序列预测系统，截至 2026-09-14，以平均 MASE 排名计，在 GIFT-Eval 基准测试的 130 个参评方案中位列第 3。排在其前的两个方案属于排行榜的 Agent 类别，即使用 Agent 或语言模型进行推理、生成或选择预测结果的多步骤系统。TW3Cast 不运行 Agent，也不运行语言模型。它的选择机制是一张仅在训练集划分上计算一次、随后冻结的表格；其专家模型则是在这些训练集划分上进行轻量微调的公开基础模型。对于 97 种数据集、频率和预测跨度配置中的每一种，该表格会提供以下四种模式之一：专家模型，即对 Chronos-2、TiRex 或 Toto 进行 LoRA 或全量微调的模型，其训练数据已通过明确规则完成清洗和增强；包含专家模型的分位数融合；基础模型融合；或在从训练集划分出的回测集上进行的选择锦标赛。表中的每一项决策都在该回测集上作出。只要专家模型在该回测中击败锦标赛方案，就会被纳入，因此一个候选模型仅需占用数 MB 存储空间和数分钟 GPU 时间，而失败的候选模型不会改变任何结果。三项受保护机制用于防止选择过程受自身偏差影响：同时衡量准确性和校准度的双重标准、针对训练期间见过该序列的候选模型设置的非对称优势门槛，以及保守的逐窗口门控机制。选择规则本身则在时间序列元回测中确定。单独使用最佳基础模型时，平均 MASE 排名为 33.8；在每种配置中均采用锦标赛方案时为 38.0；完整路由器则达到 19.4。作者发布了路由表、专家索引、固定的基础模型修订版本、提交的成绩文件以及带日期的公开分数快照；论文中的每一项排行榜数值都可通过一个脚本从这些材料中重新生成。

Abstract:TW3Cast is a time-series forecasting system that reaches position 3 of 130 entries on the GIFT-Eval benchmark by mean MASE rank, as of 2026-09-14. The two entries above it belong to the leaderboard's agentic category, multi-step systems that use agents or language models to reason about, generate or select forecasts. TW3Cast runs no agent and no language model. Its selection is a table computed once on the training split and then frozen, and its experts are public foundation models lightly fine-tuned on those training splits. For each of the 97 dataset, frequency and horizon configurations, the table serves one of four modes: a specialist, which is a LoRA or full fine-tune of Chronos-2, TiRex or Toto whose training data was cleaned and enriched by explicit rules; a quantile blend that contains a specialist; a blend of base models; or a selection tournament played on a backtest carved from the training split. Every decision in the table was taken on that backtest. A specialist is admitted the moment it beats the tournament there, so a candidate costs a few megabytes and minutes of GPU time, and a failed candidate changes nothing. Three guarded mechanisms protect the selection from its own biases: a dual accuracy and calibration criterion, an asymmetric margin against candidates that saw the series during training, and conservative per-window gates. The selection rules themselves were chosen inside a temporal meta-backtest. The best base model served alone reaches a mean MASE rank of 33.8, the tournament served on every configuration reaches 38.0, and the full router reaches 19.4. The routing table, the expert index, the pinned base-model revisions, the submitted score file and the dated snapshot of the public scores are released, and every leaderboard number in this paper regenerates from them by one script.