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
this https URL

构建时间序列预测的基础模型并研究其缩放行为，我们提出了 Lag-Llama，这是一个通用的单变量概率时间序列预测模型，它是在大量时间序列数据上训练而成的。该模型表现出了良好的零样本预测能力，在未见过的 “超出分布范围” 的时间序列数据上优于监督基线模型。我们使用平滑断裂幂律来拟合和预测模型的缩放行为。开源代码可在此网址获取。