AI RESEARCH
MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
arXiv CS.LG
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ArXi:2604.06473v1 Announce Type: new 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.