ComPass: Demand-Guided Latent Compression for High-Dimensional Time Series Forecasting

High-dimensional time series forecasting requires capturing cross-variable dependencies while maintaining computational efficiency. Channel-independent methods scale well but overlook cross-variable dependencies, whereas channel-dependent methods become increasingly expensive as the number of variables grows. Channel compression offers a promising solution, yet existing approaches apply a shared compression scheme without accounting for heterogeneous representational demands across variables. To address this limitation, we propose ComPass, a demand-guided latent compression framework that allocates limited latent capacity according to variable demands. Specifically, a demand analyzer evaluates each variable through a lightweight shared temporal bottleneck, and a budget router converts the resulting scores into continuous budget priors. Attention biases derived from demand guide learnable latent queries, while an alignment objective encourages attention usage to follow the budget priors. To preserve information useful for forecasting under differentiated capacity allocation, we further introduce a budget-conditioned prediction preservation mechanism. During training, this mechanism employs an auxiliary head to generate forecasts from recovered variable representations and their corresponding budget embeddings. The auxiliary forecasts are aligned with outputs from the main forecasting head, which is supervised by the forecasting targets. This prediction consistency favors compressed representations that retain information useful for forecasting under the allocated budgets, without adding inference overhead. Experiments on 16 high-dimensional forecasting benchmarks demonstrate that ComPass achieves strong forecasting performance while handling time series with up to 20,000 variables.