MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's quadratic sequence complexity with quadratic channel scaling, making full cross-channel attention impractical for high-dimensional time series. We propose Multivariate Infini Compressive Attention (MICA), an architectural design to extend channel-independent Transformers to channel-dependent forecasting. By adapting efficient attention techniques from the sequence dimension to the channel dimension, MICA adds a cross-channel attention mechanism to channel-independent backbones that scales linearly with channel count and context length. We e...
| Authors | Willa Potosnak, Nina Żukowska, Michał Wiliński et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 11 May 2026 |
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