Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.

翻译：为构建时间序列预测的基础模型并研究其扩展行为，我们在此介绍正在进行中的工作——Lag-Llama，一种在大规模时间序列数据集上训练的通用单变量概率时间序列预测模型。该模型在未见过的"分布外"时间序列数据集上展现出良好的零样本预测能力，且性能优于有监督基线方法。我们采用平滑分段幂律函数来拟合并预测模型的扩展行为。开源代码已发布于 https://github.com/kashif/pytorch-transformer-ts。