Efficient High-Dimensional Time Series Forecasting with Transformers: A Channel Reordering Perspective

Time series forecasting is crucial for the development of sophisticated web technologies, driving smarter, more responsive, and data-driven web applications. A key to accurate forecasting lies in effectively capturing the intricate dependencies among different variables (channels). While existing channel-dependent methods have shown strong performance by explicitly modeling inter-channel relationships, they face two critical challenges when applied to high-dimensional datasets with thousands of channels. First, the computational complexity of them grows quadratically with the number of channels, leading to significant scalability issues. Second, attention weights reveal that inter-channel dependencies exhibit both local clusters and global structures, yet current methods fail to disentangle these heterogeneous patterns, resulting in mutual interference and degraded forecasting accuracy. To address these challenges, we propose a novel Channel Reordering-Aligned group Fusion Transformer (CRAFT) for high-dimensional time series forecasting. Specifically, we design an energy-based channel reordering mechanism that reorganizes channels into a minimal-energy state, preserving inherent local-global structures. Building on reordered structure, we introduce a group fusion Transformer that explicitly separates local and global dependencies, significantly reducing computational complexity while enhancing representational clarity. Experiments on high-dimensional datasets demonstrate that CRAFT consistently outperforms baselines, achieving higher forecasting accuracy with lower computational overhead.