🤖 AI Summary
Existing time series forecasting methods often exhibit delayed responses under distribution shifts or regime changes, leading to error accumulation and reduced reliability around turning points. To address this limitation, this work proposes the L-Drive framework, which explicitly models dynamic system evolution by integrating time-varying implicit context with gated incremental representations for the first time. Furthermore, L-Drive introduces piecewise-shared relative positional basis functions to enhance intra-segment structural modeling and mitigate overfitting caused by reliance on absolute positional encoding. Experimental results demonstrate that L-Drive achieves significantly improved prediction accuracy across multiple datasets while maintaining computational efficiency.
📝 Abstract
Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm. They learn a unified mapping from history to the future in the observation space to fit value-level dependencies. However, real-world systems often undergo distribution shifts and regime changes. In such cases, a unified mapping can exhibit response lag around turning points, causing error accumulation within the switching window and reducing forecasting reliability. To address this issue, we propose L-Drive, a change-aware forecasting framework. L-Drive introduces a Latent-Context, to explicitly characterize high-level dynamics evolving over time, and uses gating to modulate increment representations. This provides more timely change cues and improves adaptation to changing segments. In addition, it incorporates patch-shared relative positional basis functions to strengthen intra-segment structural modeling and reduce overfitting caused by absolute-position memorization. Extensive experiments validate the effectiveness of L-Drive and show a better overall trade-off between forecasting accuracy and computational efficiency.