Inspired by Large Language Models (LLMs), Time Series Forecasting (TSF), a
long-standing task in time series analysis, is undergoing a transition towards
Large Time Series Models (LTSMs), aiming to train universal transformer-based
models for TSF. However, training LTSMs on heterogeneous time series data poses
unique challenges, including diverse frequencies, dimensions, and patterns
across datasets. Recent endeavors have studied and evaluated various design
choices aimed at enhancing LTSM training and generalization capabilities,
spanning pre-processing techniques, model configurations, and dataset
configurations. In this work, we comprehensively analyze these design choices
and aim to identify the best practices for training LTSM. Moreover, we propose
\emph{time series prompt}, a novel statistical prompting strategy tailored to
time series data. Furthermore, based on the observations in our analysis, we
introduce \texttt{LTSM-bundle}, which bundles the best design choices we have
identified. Empirical results demonstrate that \texttt{LTSM-bundle} achieves
superior zero-shot and few-shot performances compared to state-of-the-art LSTMs
and traditional TSF methods on benchmark datasets.

基于大型语言模型（LLMs）的启发，时间序列预测（TSF）正在向基于 Universal-Transformer 的大型时间序列模型（LTSMs）过渡，旨在为 TSF 训练通用模型。然而，在异构时间序列数据上训练 LTSMs 面临着多样的挑战，包括数据集之间不同的频率，维度和模式。我们通过综合分析设计选择，并旨在确定训练 LTSM 的最佳实践。此外，我们提出了一种针对时间序列数据的新型统计提示策略称为 “时间序列提示”。基于我们分析中的观察结果，我们引入了 “LTSM-bundle”，该捆绑包绑定了我们确定的最佳设计选择。实证结果表明，“LTSM-bundle” 在基准数据集上相比于最先进的 LSTMs 和传统的 TSF 方法具有卓越的零射击和少射击性能。