统一的超参优化流程，让Transformer时序模型更好用。
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
- 构建通用超参优化流水线，适配多种时序模型
- 在多个基准数据集上验证，提升模型性能表现
- 适合工业界和学术界快速找到最优超参数
近年来，基于Transformer的时序预测（TSF）模型因其高效性和通用性受到广泛关注。然而，这些模型通常需要大量超参数优化（HPO）才能达到最佳性能，而针对基于Transformer的TSF缺乏统一的优化流程。本文提出一种统一的超参优化管道，并在多个先进SOTA Transformer TSF模型上进行广泛实验。实验基于标准基准数据集，评估并比较不同模型的表现，获得实用洞见与实例。该管道具有泛化能力，可扩展至其他SOTA模型，如Mamba和TimeMixer，实验已证实其有效性。本工作旨在为工业界从业者与学术研究者提供高效识别适配特定领域应用最优超参数的指导。代码与完整实验结果已在GitHub公开。
原文摘要 · Abstract (English)
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require extensive hyperparameter optimization (HPO) to achieve the best possible performance, and a unified pipeline for HPO in transformer-based TSF remains lacking. In this paper, we present one such pipeline and conduct extensive experiments on several state-of-the-art (SOTA) transformer-based TSF models. These experiments are conducted on standard benchmark datasets to evaluate and compare the performance of different models, generating practical insights and examples. Our pipeline is generalizable beyond transformer-based architectures and can be applied to other SOTA models, such as Mamba and TimeMixer, as demonstrated in our experiments. The goal of this work is to provide valuable guidance to both industry practitioners and academic researchers in efficiently identifying optimal hyperparameters suited to their specific domain applications. The code and complete experimental results are available on GitHub.
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