Recent advances in long-term time series forecasting have introduced numerous complex prediction models that consistently outperform previously published architectures. However, this rapid progression raises concerns regarding inconsistent benchmarking and reporting practices, which may undermine the reliability of these comparisons. Our position emphasizes the need to shift focus away from pursuing ever-more complex models and towards enhancing benchmarking practices through rigorous and standardized evaluation methods. To support our claim, we first perform a broad, thorough, and reproducible evaluation of the top-performing models on the most popular benchmark by training 3,500+ networks over 14 datasets. Then, through a comprehensive analysis, we find that slight changes to experimental setups or current evaluation metrics drastically shift the common belief that newly published results are advancing the state of the art. Our findings suggest the need for rigorous and standardized evaluation methods that enable more substantiated claims, including reproducible hyperparameter setups and statistical testing.

本研究关注长期时间序列预测中的模型性能评估问题，提出现有的基准评估方法不一致，影响了模型比较的可靠性。通过对3500多个模型在14个数据集上的广泛评估，研究发现实验设置或评估指标的细微变化会显著影响结果的解读，强调了采用严格标准化评估方法的必要性，以增强结果的可信度和可重复性。

定位：长期时间序列预测中没有冠军