Are Language Models Actually Useful for Time Series Forecasting? (2406.16964v2)

Abstract: LLMs are being applied to time series forecasting. But are LLMs actually useful for time series? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even improve! We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters.

• The paper demonstrates that ablated models achieve comparable or superior forecasting accuracy compared to full LLM-based methods.
• The paper shows that LLMs incur substantial computational overhead, making simpler architectures more efficient in training and inference.
• The paper reveals that pretraining on textual data does not enhance forecasting performance, indicating limited benefits for time series tasks.

Evaluating the Utility of LLMs in Time Series Forecasting Tasks

The paper "Are LLMs Actually Useful for Time Series?" investigates the viability of leveraging LLMs for performing time series forecasting. Despite the growing trend to apply LLMs to time series tasks, this paper presents a series of ablation and comparative analyses which suggest that the complexity of such models may not yield commensurate improvements in performance and may indeed be inefficient in terms of computational cost.

Key Findings

Performance of LLM-based Methods vs. Ablated Versions

The paper evaluates three recent state-of-the-art LLM-based methods for time series forecasting: OneFitAll, Time-LLM, and LLaTA. Each method is subjected to three ablation scenarios: removing the LLM component entirely, replacing the LLM with a multi-head attention layer, and replacing the LLM with a simple transformer block. The results consistently show that these ablated models perform comparably or better than their LLM-based counterparts.

For instance, ablations outperformed Time-LLM, LLaTA, and OneFitsAll in 26/26, 22/26, and 19/26 cases, respectively, across various performance metrics and datasets. Notably, detailed 95% confidence intervals indicate that the performance overlap between simplified and LLM models is statistically significant, underscoring that LLMs do not provide substantial benefits for these tasks.

Computational Cost

The computational overhead brought about by LLMs is substantial. Time-LLM, with 6642 million parameters, significantly increases both training and inference times. The evaluation indicates that simpler models can reduce the training time by up to three orders of magnitude while maintaining or improving forecasting performance. Ablated models are typically found to be faster and more efficient, highlighting a stark contrast when compared to their LLM-based versions.

Contributions of Pretraining and Sequential Dependencies

A significant thrust of the analysis involves understanding whether pretraining LLMs on textual data can benefit time series forecasting. Results reveal that randomly initialized LLMs perform on par with pretrained ones, suggesting that pretraining on textual corpora does not confer a distinct advantage for time series tasks. Furthermore, evaluations involving shuffled and masked input sequences show that LLM-based models do not effectively capture sequential dependencies beyond what non-LLM models achieve.

Few-shot Learning and Encoding Approaches

Despite the known success of LLMs in few-shot and transfer learning, the paper demonstrates that ablated models match or exceed the performance of LLM-based methods even when trained on just 10% of the training data. This finding holds significant implications for scenarios with limited data availability.

The paper also explores various encoding strategies to understand the sources of performance in LLM-based models. It concludes that encoding techniques like patching combined with multi-head attention or simple transformers can yield effective representations, obviating the need for the full complexity of LLMs.

Implications and Future Directions

The findings indicate that LLMs may not justify their computational costs for traditional time series forecasting tasks. This divergence in anticipated versus actual utility invites researchers to re-evaluate the application contexts where LLMs are genuinely advantageous. Future developments may focus on hybrid or multimodal applications where the innate capabilities of LLMs in understanding natural language can complement time series data, as suggested by emerging applications in social understanding or more general time series reasoning tasks.

Conclusion

By systematically dismantling popular LLM-based time series forecasting models, this paper critically reassesses the role of LLMs in such contexts, highlighting simpler yet equally robust alternatives. These insights serve to guide researchers in developing more efficient and effective time series models, encouraging a balanced approach between leveraging advanced LLMs and ensuring computational feasibility.