提出L-Drive框架，用隐状态上下文提升时序预测对系统突变的响应速度
L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
- 引入隐状态上下文捕捉动态变化，通过门控机制及时调整预测
- 在多个数据集上实现更优的准确率与效率平衡，尤其在突变窗口表现更稳定
- 适合需要快速适应环境变化的工业时序预测场景
主流多变量时间序列预测方法普遍采用直接映射范式，在观测空间中学习历史到未来的统一映射以捕捉值级依赖。然而真实系统常经历分布漂移和模式切换，此时统一映射在转折点附近会出现响应延迟，导致切换窗口内误差累积，降低预测可靠性。为此，我们提出L-Drive，一种感知变化的预测框架。L-Drive引入隐状态上下文（Latent-Context），显式刻画随时间演化的高层动态，并通过门控机制调节增量表示，提供更及时的变化线索，提升对变化区段的适应能力。此外，它采用共享块的相对位置基函数，强化段内结构建模，减少由绝对位置记忆引起的过拟合。大量实验验证了L-Drive的有效性，展现出在预测精度与计算效率之间的更优权衡。
原文摘要 · Abstract (English)
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.
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