Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
arXiv:2605.30376v1 Announce Type: new Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bounded'', struggling to generalize across heterogeneous datasets.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 latent prototype codebook that decouples correlation
The proliferation of high-dimensional time series data across various industries necessitates more efficient and accurate forecasting methods.
Unicorn addresses a fundamental bottleneck in AI for time series analysis, potentially enabling more robust and scalable predictive modeling across complex systems.
The ability to perform multi-dataset pretraining on high-dimensional time series, decoupling correlation modeling from channel-dependency issues, represents a significant advancement.
- · AI/ML research community
- · Industries relying on time series forecasting (finance, logistics, healthcare)
- · Cloud providers offering AI/ML services
- · Providers of less scalable time series forecasting models
Improved accuracy and efficiency in forecasting high-dimensional time series.
Accelerated development of AI applications in domains previously limited by time series complexity.
Enhanced operational efficiencies and risk management across sectors due to better predictive capabilities.
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Read at arXiv cs.LG