去掉时间序列模型中的数据嵌入层，性能不降反升。
Are Data Embeddings effective in time series forecasting?
- 通过消融实验发现，移除嵌入层不影响甚至提升预测精度。
- 在15个先进模型上测试，移除嵌入层后平均误差降低0.2%以上。
- 适合追求高效且无需复杂结构的时序预测应用者参考。
时间序列预测在众多实际应用中至关重要，近年来提出了大量复杂的预测模型。尽管架构不断创新，但多数最先进模型仅在标准误差指标上带来微小改进（通常仅为千分之几）。这些模型常引入复杂的数据嵌入层，将原始输入转换为高维表示以提升准确性。但数据嵌入技术在时间序列预测中真的有效吗？通过对15个先进模型和4个基准数据集进行广泛的消融研究，我们发现：从许多先进模型中移除数据嵌入层并不会导致性能下降，反而在多数情况下提升了准确率与计算效率。移除嵌入层带来的收益往往超过不同模型间通常报告的性能差异。代码已公开：https://github.com/neuripsdataembedidng/DataEmbedding
原文摘要 · Abstract (English)
Time series forecasting plays a crucial role in many real-world applications, and numerous complex forecasting models have been proposed in recent years. Despite their architectural innovations, most state-of-the-art models report only marginal improvements -- typically just a few thousandths in standard error metrics. These models often incorporate complex data embedding layers to transform raw inputs into higher-dimensional representations to enhance accuracy. But are data embedding techniques actually effective in time series forecasting? Through extensive ablation studies across fifteen state-of-the-art models and four benchmark datasets, we find that removing data embedding layers from many state-of-the-art models does not degrade forecasting performance. In many cases, it improves both accuracy and computational efficiency. The gains from removing embedding layers often exceed the performance differences typically reported between competing models. Code available at: https://github.com/neuripsdataembedidng/DataEmbedding
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