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Channel-Aware Low-Rank Adaptation in Time Series Forecasting

Tong Nie, Yuewen Mei, Guoyang Qin, Jian Sun, Wei Ma

TL;DR

Long-term multivariate time-series forecasting often faces a robustness-versus-capacity dilemma between channel-independent (CI) and channel-dependent (CD) approaches. The paper introduces Channel-Aware Low-Rank Adaptation (C-LoRA), a plug-in that decorates a shared CD backbone with per-channel low-rank adapters conditioned on channel identity, yielding a hybrid that preserves cross-channel interactions while maintaining channel-specific patterns. Across seven real-world datasets and diverse backbones, C-LoRA consistently improves performance, with larger gains on datasets featuring heterogeneous channels, while remaining parameter-efficient. The approach supports transfer via fine-tuning of C-LoRA and offers insights into channel identity preservation through attention maps, presenting a practical pathway to exploit cross-channel dependencies in time-series forecasting.

Abstract

The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions. The latter is more expressive for representing complex cross-channel dependencies, but is prone to overfitting. To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. As a plug-in solution, it is adaptable for a wide range of backbone architectures. Extensive experiments show that it can consistently and significantly improve the performance of both CI and CD models with demonstrated efficiency and flexibility. The code is available at https://github.com/tongnie/C-LoRA.

Channel-Aware Low-Rank Adaptation in Time Series Forecasting

TL;DR

Long-term multivariate time-series forecasting often faces a robustness-versus-capacity dilemma between channel-independent (CI) and channel-dependent (CD) approaches. The paper introduces Channel-Aware Low-Rank Adaptation (C-LoRA), a plug-in that decorates a shared CD backbone with per-channel low-rank adapters conditioned on channel identity, yielding a hybrid that preserves cross-channel interactions while maintaining channel-specific patterns. Across seven real-world datasets and diverse backbones, C-LoRA consistently improves performance, with larger gains on datasets featuring heterogeneous channels, while remaining parameter-efficient. The approach supports transfer via fine-tuning of C-LoRA and offers insights into channel identity preservation through attention maps, presenting a practical pathway to exploit cross-channel dependencies in time-series forecasting.

Abstract

The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment and has been shown to be more robust to distribution shifts, but lacks sufficient capacity to model meaningful channel interactions. The latter is more expressive for representing complex cross-channel dependencies, but is prone to overfitting. To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. As a plug-in solution, it is adaptable for a wide range of backbone architectures. Extensive experiments show that it can consistently and significantly improve the performance of both CI and CD models with demonstrated efficiency and flexibility. The code is available at https://github.com/tongnie/C-LoRA.
Paper Structure (11 sections, 7 equations, 7 figures, 1 table)

This paper contains 11 sections, 7 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Methodology
  4. General Forecasting Backbone
  5. Channel-Aware Low-Rank Adaptation
  6. Experiments
  7. Experimental Setup
  8. Performance Improvement by C-LoRA
  9. Parameter Efficiency
  10. Discussion and Analysis
  11. Conclusion

Figures (7)

  • Figure 1: The proposed channel-aware low-rank adaptation.
  • Figure 2: Parameter comparison of different strategies.
  • Figure 3: MSE under different lengths of look-back window.
  • Figure 4: Transfer across datasets by fine-tuning C-LoRA.
  • Figure 5: randomly shuffling the order of channels (Solar).
  • ...and 2 more figures