L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting

• URL: http://arxiv.org/abs/2605.17730v2
• Date: Mon, 25 May 2026 12:39:48 GMT
• Title: L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
• Abstract summary: We propose L-Drive, a change-aware forecasting framework.<n>It provides more timely change cues and improves adaptation to changing segments.<n>Experiments validate the effectiveness of L-Drive and show a better overall trade-off between forecasting accuracy and computational efficiency.
• Score: 6.726418089422619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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