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

I recently worked through the architecture of t0-alpha, a 101.6M-parameter foundation model for time-series forecasting. The design choice I found most interesting is that it separates two kinds of reasoning: Time attention learns how each variable evolves across time. Group attention allows related variables to exchange information. The rest of the architecture, briefly: i

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PossiblePossibly related (embedding) · 52%Self-Gating Attention for Efficient Time Series Forecasting →
PossiblePossibly related (embedding) · 51%How Good Can Linear Models Be for Time-Series Forecasting? →
PossiblePossibly related (embedding) · 49%engineering87/llm-atlas →
PossiblePossibly related (embedding) · 46%Extreme Adaptive Transformer for Time Series Forecasting →
PossiblePossibly related (embedding) · 46%Nixtla/mlforecast →

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paperSelf-Gating Attention for Efficient Time Series Forecasting paperHow Good Can Linear Models Be for Time-Series Forecasting?repoengineering87/llm-atlas paperExtreme Adaptive Transformer for Time Series Forecasting repoNixtla/mlforecast

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paperExtreme Adaptive Transformer for Time Series Forecasting repoengineering87/llm-atlas repoNixtla/mlforecast paperHow Good Can Linear Models Be for Time-Series Forecasting?paperSelf-Gating Attention for Efficient Time Series Forecasting