How a 102M Parameter Time-Series Model Predicts
sjm213 · reddit · 2026-07-15
The author breaks down t0-alpha, a time-series foundation model with roughly 101.6M parameters, focusing on its approach to multivariate forecasting.
Architecture Highlights
- Slices time series into patches of length 32
- Maps each patch into a 512-dimensional representation
- Features 24 Transformer layers: 16 time-attention + 8 group-attention layers
- Utilizes time-aware rotary embeddings, RMSNorm, and SwiGLU
- Outputs 9 quantiles for probabilistic forecasting
- Supports a maximum context window of 1,024 time steps
Results & Questions
- Achieves an aggregate CRPS of 0.4941 on GIFT-Eval
- This performance is roughly on par with TimesFM 2.5 and Chronos-2, but with only about 102M parameters
The author concludes with two discussion points:
- Does decoupling temporal dimensions from inter-variable information actually provide useful inductive biases?
- Can smaller, specialized foundation models continue to compete with much larger forecasting models?
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