Scaling-laws for Large Time-series Models (2405.13867v2)

Abstract: Scaling laws for LLMs have provided useful guidance in training ever larger models for predictable performance gains. Time series forecasting shares a similar sequential structure to language, and is amenable to large-scale transformer architectures. Here we show that foundational decoder-only time series transformer models exhibit analogous scaling-behavior to LLMs, with architectural details (aspect ratio and number of heads) having a minimal effect over broad ranges. We assemble a large corpus of heterogenous time series data on which to train, and establish for the first time power-law scaling with parameter count, dataset size, and training compute, spanning five orders of magnitude.

• The paper demonstrates that decoder-only transformer time-series models follow power-law scaling behaviors akin to those observed in large language models.
• The study systematically investigates the impacts of model parameters, compute resources, and dataset size using a heterogeneous corpus of over 8 billion data points.
• The findings offer actionable guidelines for optimizing resource allocation in developing large-scale, high-performance time-series forecasting models.

Overview of the Paper: Scaling Laws for Large Time-series Models

The paper "Scaling-laws for Large Time-series Models," authored by Thomas D. P. Edwards et al., addresses the subject of large-scale models for time-series forecasting. It aims to extend the scaling laws known from LLMs to foundational time-series models. The authors present a detailed investigation into the scaling behaviors concerning model parameters, dataset size, and computation resources in relation to the test performance of large time-series transformers.

Key Insights and Methodology

A crucial insight from this work is that time-series models based on decoder-only transformer architectures exhibit power-law scaling behaviors similar to those of LLMs. The researchers leverage a large, heterogeneous corpus of worldwide data to conduct their experiments. This corpus, which comprises about 8 billion data points across over 30 million individual time-series, allows for a comprehensive investigation of scaling behaviors across five orders of magnitude.

Key parameters in their analysis include the number of parameters in the model, the compute resources allocated for training, and the size of the dataset. Their findings suggest a consistent power-law scaling of performance metrics—Mean-Square Error (MSE), Continuous Ranked Probability Score (CRPS), and log-likelihood—with these factors. The work highlights how models improve with increased size and computational availability, a consistency also seen in LLMs.

Experimental Framework

The authors use a decoder-only transformer with a learned positional encoding and a Student's-t distribution head, specifically designed for probabilistic forecasting tasks. They opt for a negative log-likelihood loss function during training. What stands out is their systematic investigation of the optimal architecture settings, such as aspect ratio and number of attention heads, finding these to have minimal impact on performance compared to parameters count.

The empirical results demonstrated in the paper include detailed plots of the scaling behavior with respect to model parameters, compute resources, and dataset size. Particularly notable is the observation that the performance metrics follow power-law behavior, albeit with minor deviations at lower scales.

Practical and Theoretical Implications

From a practical standpoint, these scaling laws serve as pivotal guidelines for the allocation of resources in the development of large-scale time-series models. The foundational models capable of zero-shot prediction across various domains underline the potential to replace traditional statistical or domain-specific models in certain scenarios.

Theoretically, this research contributes to the broader understanding of neural scaling laws beyond natural language processing. It paves the way for further explorations into how foundational time-series models can be optimized for performance and scalability.

Future Directions

The paper acknowledges the need for expanding research to include multivariate time-series predictions and longer context lengths to better capture low-frequency variations. Furthermore, the authors underscore the desire to explore alternative distribution heads and context-length scaling to further improve model performance.

A notable prospective research avenue is the development of a robust framework for assessing data diversity, which the authors highlight as a critical factor influencing the efficacy of large-scale training.

Conclusion

This work by Edwards et al. provides a thorough examination of the scaling laws relevant to large time-series models, paralleling those observed in LLMs. It emphasizes the viability of employing foundational models in time-series forecasting, fostering advancements in AI-driven decision-making across diverse fields like climate science, healthcare, and finance. As the field progresses, the findings in this paper will likely guide subsequent efforts to refine and implement large-scale time-series forecasting models.