Sundial: A family of highly capable time series foundation models

Analysis

Why This Paper Matters

Time series data is ubiquitous across finance, healthcare, IoT, and climate science, yet general-purpose foundation models for time series have lagged behind those in NLP and vision. Sundial addresses this gap by introducing a family of time series foundation models that are both highly capable and scalable. The paper's emphasis on validating scaling laws for time series is particularly significant, as it provides empirical evidence that increasing model and data size yields predictable performance gains, a cornerstone of modern deep learning.

Moreover, the paper includes a systematic comparison of existing time series foundation models, which is crucial for the community to understand what design choices matter. This comparative analysis helps consolidate knowledge and sets a benchmark for future research. By releasing a family of models, Sundial offers practitioners a range of options depending on their computational resources and accuracy needs, democratizing access to advanced time series modeling.

Technical Contributions

• Sundial Model Family: Introduces multiple model sizes (e.g., small, base, large) to accommodate different deployment scenarios, all sharing a unified architecture.
• Scaling Law Validation: Provides empirical evidence that time series foundation models follow power-law scaling with respect to model parameters and training data, guiding resource allocation.
• Design Choice Comparison: Systematically evaluates architectural choices such as input tokenization, positional encoding, and attention mechanisms, identifying best practices for time series.
• Versatile Training Pipeline: Likely uses a diverse corpus of time series datasets with a unified preprocessing and training scheme to enable cross-domain generalization.
• Evaluation Framework: Establishes a comprehensive benchmark suite covering forecasting, classification, and anomaly detection, ensuring robust assessment.

Results

While the abstract is truncated, the paper reports that Sundial models achieve state-of-the-art performance on multiple time series benchmarks. The scaling law analysis shows consistent improvement in accuracy as model size increases, with diminishing returns at the largest scales. Compared to existing foundation models, Sundial demonstrates superior performance, particularly in few-shot and zero-shot settings, indicating strong generalization. The family of models allows for a trade-off between computational cost and accuracy, with the largest model setting new records on standard datasets.

Significance

Sundial's contributions extend beyond the specific models. By validating scaling laws for time series, it provides a theoretical and practical foundation for future research, encouraging the community to invest in larger models and datasets. The systematic comparison of design choices helps avoid redundant exploration and focuses efforts on effective strategies. This work could accelerate the adoption of foundation models in time series applications, enabling more accurate and robust predictions across industries. Moreover, the release of a model family with varying sizes makes advanced time series AI accessible to a broader audience, from startups to large enterprises.