Datadog发布Toto 2.0时间序列基础模型系列，权重开源（Apache 2.0），参数规模从4M到2.5B，性能随规模提升，在BOOM、GIFT-Eval、TIME基准上取得领先。该模型首次在时间序列领域展示了缩放定律。
Are scaling laws finally working for time series foundation models?
Today, @datadoghq is releasing Toto 2.0 weights in Apache 2.0 on @huggingface. It's a family of open-weights TSFMs from 4M to 2.5B parameters, where every size beats the last from a single hyperparameter config. First across the leading benchmarks: BOOM, GIFT-Eval, and TIME.
Most TSFM families ship multiple sizes that all perform roughly the same. This one doesn't.
Why it matters: scaling laws gave language and vision a predictable relationship between compute, data, parameters, and downstream performance. Time series hasn't had that curve until now. Once you have it, you can scale data and compute with confidence, and start asking which new capabilities emerge at the next order of magnitude.
2.5B open-source weights: https://t.co/prpcGoCw0U
4M open-source weights: https://t.co/5d6rw5NYL2
Blogpost: https://t.co/xKazgTMh1I
likes: 142 | retweets: 23 | replies: 6 | views: 13682