Toto-2.0: Time Series Multivariate Forecasting Finally Scales Like LLMs

Reddit r/ArtificialInteligence 07/09/26, 11:44 AM Models

Summary

Datadog released Toto-2.0, a time series model that scales like LLMs, featuring Contiguous Patch Masking and Arcsinh normalization for improved forecasting.

Datadog research recently released Toto-2.0, their new time series model. The model features some unique properties compared to its previous version Toto-1.0: Contiguous Patch Masking (CPM) replaces autoregressive decoding with a single parallel forward pass. Arcsinh normalization keeps small fluctuations visible while compressing extreme spikes - perfect for sparse data. NorMuon optimizer handles the sign-valued gradients of pinball loss far better than AdamW. u-µP hyperparameter transfer tunes settings once on a 10M proxy model and reuses them across all 5 target sizes. Full discussion and tutorial about the model here

Original Article

Similar Articles

Toto 2.0: Time series forecasting enters the scaling era (13 minute read)

TLDR AI

DataDog releases Toto 2.0, an open-weights family of time series foundation models ranging from 4M to 2.5B parameters, demonstrating consistent scaling improvements and achieving state-of-the-art results on multiple benchmarks including BOOM, GIFT-Eval, and TIME.

@ClementDelangue: Are scaling laws finally working for time series foundation models? Today, @datadoghq is releasing Toto 2.0 weights in …

X AI KOLs Following

Datadog releases Toto 2.0, a family of open-weights time series foundation models from 4M to 2.5B parameters, achieving state-of-the-art results on three benchmarks. The models demonstrate scaling laws for time series, improving predictably with parameter count.

How does a 102M-parameter transformer forecast multivariate time series?

Reddit r/artificial

This article provides a visual walkthrough of t0-alpha, a 101.6M-parameter foundation model for multivariate time-series forecasting that separates time attention from cross-variable group attention, achieving competitive CRPS scores on GIFT-Eval compared to larger models like TimesFM 2.5 and Chronos-2.

TA-SparseMG: Trend-Aware Sparse Forecasting via Multi-Scale Gating for Long-Term Time Series

arXiv cs.LG

This paper proposes TA-SparseMG, a lightweight cross-period forecasting model for long-term time series that incorporates trend-aware reversible instance normalization, scale-adaptive gated denoising, and a multiscale gated-attention MLP module to address nonstationarity and high-frequency disturbances.

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

arXiv cs.AI

Proposes a domain-agnostic framework for generating grounded natural language explanations for time series forecasts using large language models, reducing hallucination by constraining to verifiable evidence. Evaluated on financial and freight pricing case studies.