Unicorn: Universal Correlation Modeling for High-Dimensional Time Series Forecasting

Modern time series architectures face a fundamental trade-off: channel-independent models scale well across datasets but ignore inter-channel dependencies, while many channel-dependent models learn correlations within individual datasets and face computational or transfer challenges when channel sets change. To bridge this gap, we introduce Unicorn (Universal Correlation Network), a framework for scalable, multi-dataset pretraining on high-dimensional time series. At the core of Unicorn is a continuous prototype bank that decouples correlation modeling from specific channel identities. By routing heterogeneous channels through a shared latent space, Unicorn learns identity-agnostic, reusable interaction patterns with linear interaction cost in the number of channels, enabling joint pretraining across different channel sets. Experiments on financial and public forecasting benchmarks show competitive forecasting accuracy and strong data efficiency during finetuning, offering a scalable path toward multivariate time series foundation models.