HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting

2026-06-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

HAMON introduces a novel passive diffractive optical forecasting core designed for long-horizon time-series prediction. This system encodes historical data onto an optical aperture, leaving future positions dark, and employs cascaded trainable phase masks with free-space diffraction to directly generate forecasts in the output field. During inference, HAMON performs prediction via a single passive optical propagation pass, eliminating the need for trainable digital sequence-mixing layers. Benchmarking shows HAMON outperforms leading digital baselines on ETTm2 across all horizons and on ETTh2 at all but the longest horizon, achieving up to a 14% improvement in Mean Squared Error (MSE) consistently. It also demonstrates competitive performance on the Weather dataset, though it trails on other ETT configurations and high-channel-count Traffic and Electricity datasets. Ablation studies confirm forecasts originate from the data-bearing optical field, positioning HAMON as a concrete target for optical hardware development and passive physical sequence mixing.

Key takeaway

For AI Hardware Engineers exploring novel computing architectures, HAMON suggests that your focus on digital temporal mixing for forecasting might be too narrow. You should investigate passive optical systems as a viable alternative, especially for long-horizon time-series tasks where they can offer significant performance gains, such as the 14% MSE improvement seen on ETTm2. Consider prototyping optical cores to validate their efficiency and scalability for specific forecasting applications.

Key insights

HAMON demonstrates that passive optical systems can effectively perform long-horizon time-series forecasting, outperforming digital baselines in specific benchmarks.

Principles

• Low-complexity forecasting may not require dense digital representations.
• Optical propagation can directly shape time-series forecasts.
• Passive physical systems offer alternatives to learned digital mixing.

Method

Encode historical values onto an optical aperture, use cascaded trainable phase masks and free-space diffraction to shape the forecast via a single passive optical propagation pass.

In practice

• Explore optical hardware for time-series prediction.
• Investigate passive physical sequence mixing designs.
• Benchmark optical systems against digital forecasting.

Topics

• Optical Computing
• Time-Series Forecasting
• Passive Optical Systems
• Diffractive Optics
• Long-Horizon Prediction
• ETTm2 Benchmark

Best for: Research Scientist, AI Scientist, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.