Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention

Key points

• A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ.1
• Its dual-axis attention runs both relations side by side in every layer, one across variables at a timestep and one across time within a variable; a learned gate weights the two per layer, and an axis-aware rotary encoding preserves both grid coordinates.1
• Morpheus-2.0 Multiscale extends the model to several temporal resolutions at once, weighting them according to the series being forecast.1
• Across GIFT-Eval, which spans several domains and frequencies, the two models give complementary operating points: Morpheus-2.0 attains 0.685 relative MASE with 81M parameters, while the larger Morpheus-2.0 Multiscale attains 0.646 relative MASE and 0.455 relative CRPS, the lowest of any published system on both metrics.1

Sources (1)

• [1]Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:37 PM

  We introduce Morpheus-2.0, a compact encoder-only foundation model that represents targets and covariates on a single variable-time grid.

  A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ.

Extractive summary: sentences quoted from the sources.