We present Tiny-TSM, a time series foundation model characterized by small scale, economical training, and state-of-the-art performance. It comprises 23M total parameters, trained on a single A100 GPU in less than a week using a new synthetic data generation and data augmentation pipeline (SynthTS). Without any neural architecture search, hyperparameter tuning, or scaling up model size, Tiny-TSM achieves state-of-the-art performance on a wide range of time series benchmark datasets, often outperforming much larger models and even matching the performance of much larger, industrial-scale, likely highly tuned foundation models. Specifically, Tiny-TSM outperforms all other time series foundation models we evaluated on medium- and long-term forecasting tasks under MSE loss, while short-term accuracy is still competitive with state-of-the-art models. We also introduce a causal input normalization scheme that enables time series models to be trained with dense next-token prediction loss, significantly accelerating convergence speed and reducing training time. All experiments were conducted on a single A100 GPU, illustrating the practicality of the proposed approach in a resource-constrained setting.

翻译：本文提出Tiny-TSM，一种具有小规模、经济训练和先进性能特点的时间序列基础模型。该模型包含总计2300万个参数，通过新型合成数据生成与数据增强流程（SynthTS），在单张A100 GPU上训练不足一周即可完成。无需任何神经架构搜索、超参数调优或扩大模型规模，Tiny-TSM在广泛的时间序列基准数据集上实现了最先进的性能，通常超越更大型模型，甚至媲美规模更大、工业级且可能经过高度调优的基础模型。具体而言，在均方误差损失下，Tiny-TSM在中长期预测任务中优于我们评估的所有其他时间序列基础模型，而短期预测精度仍与最先进模型相当。我们还引入了一种因果输入归一化方案，使时间序列模型能够通过密集的下一个标记预测损失进行训练，显著加速收敛速度并减少训练时间。所有实验均在单张A100 GPU上完成，证明了所提方法在资源受限环境中的实用性。