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CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

Published 12 Mar 2024 in cs.LG and cs.CL | (2403.07300v3)

Abstract: Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, LLMs based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However, current LLM-based MTSF methods usually focus on adapting and fine-tuning LLMs, while neglecting the distribution discrepancy between textual and temporal input tokens, thus leading to sub-optimal performance. To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning (CALF) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input. To reduce the distribution discrepancy, we develop the cross-modal match module to first align cross-modal input distributions. Additionally, to minimize the modality distribution gap in both feature and output spaces, feature regularization loss is developed to align the intermediate features between the two branches for better weight updates, while output consistency loss is introduced to allow the output representations of both branches to correspond effectively. Thanks to the modality alignment, CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks with low computational complexity, and exhibiting favorable few-shot and zero-shot abilities similar to that in LLMs. Code is available at https://github.com/Hank0626/LLaTA.

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Summary

  • The paper presents a novel CALF framework that uses cross-modal fine-tuning to bridge the gap between language models and structured time series data.
  • It employs both static (via PCA) and dynamic knowledge extraction techniques to efficiently align modalities and reduce computational demands.
  • Experimental results show significant improvements with reduced MSE and MAE, outperforming Transformer-based models in short- and long-term forecasts.

Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation

In recent developments within the field of time series forecasting, the integration of large pre-trained LLMs has emerged as a noteworthy innovation. The paper "Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation" explores an inventive approach to leveraging LLMs for time series forecasts, addressing key issues of modality misalignment that traditionally hinder such applications.

Central Contributions

The authors introduce a novel framework, designated as LLaTA (LLMs and Time series Alignment framework). This framework tackles the intrinsic challenges associated with the direct application of LLMs to time series forecasting, primarily the modality gap between structured temporal data and text-based data typically handled by LLMs. The central tenet of this research is grounded in cross-modal knowledge distillation, which is utilized to extract both static (input-agnostic) and dynamic (input-dependent) knowledge from LLMs, thus empowering the forecasting models with enhanced generalization capabilities.

Methodological Approach

At the core of LLaTA is a dual-branch architecture comprising a temporal modal branch and a textual modal branch. Key innovations include:

  • Cross-Modal Knowledge Transfer: By projecting temporal tokens into the latent space of textual tokens, the framework uses cross-modal knowledge distillation to align these modalities effectively.
  • Static Knowledge Utilization: Through a reduction technique such as Principal Component Analysis (PCA), the framework efficiently extracts influential word embeddings, mitigating the computational demands of extended vocabulary lists.
  • Dynamic Knowledge Exploration: Implementing a combination of feature regularization loss and modal consistency loss ensures that both branches work synergistically, preserving the contextual nuances captured by LLMs during the forecasting process.

Experimental Evaluation

The experimental setup includes comprehensive evaluations across eight well-established datasets, demonstrating superior performance and state-of-the-art results in both short-term and long-term forecasting scenarios. The empirical findings highlight significant reductions in metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) when compared to existing methods, including state-of-the-art Transformer-based models like PatchTST. Moreover, the framework exhibits robust capabilities in both few-shot and zero-shot learning scenarios, underscoring the model's adaptability and efficiency in data-scarce environments.

Implications and Future Work

The implications of LLaTA's contributions are manifold. Practically, the framework facilitates the extension of LLM capabilities to a broader array of forecasting tasks, enhancing their applicability in domains such as weather prediction, energy consumption, and financial modeling. Theoretically, it proposes a robust methodology for bridging disparate data modalities, leveraging the extensive pre-training of LLMs for domain-specific tasks with constrained datasets.

Future work could explore further enhancements in dynamic knowledge acquisition, perhaps integrating transformers with real-time adaptive pre-training techniques to continuously refine model performance. Additionally, expanding this framework to incorporate multi-modal data sources, beyond textual and temporal, could open avenues for richer data interactions and nuanced forecasting applications.

The research presented within this paper signifies a meaningful progression in the leveraging of LLMs for non-textual data forecasting, establishing a foundational approach that could drive subsequent advancements within this intersecting field.

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