数据嵌入在时间序列预测中是否有效？
Are Data Embeddings effective in time series forecasting?
AI总结：
本研究通过消融实验发现，移除数据嵌入层不会损害时间序列预测性能，反而常提升准确性和效率，质疑了数据嵌入的必要性。
摘要：
时间序列预测在许多实际应用中扮演着关键角色，近年来提出了众多复杂的预测模型。尽管这些模型在架构上有所创新，但大多数最先进的模型仅报告了微小的改进——通常在标准误差指标上仅提升千分之几。这些模型常常包含复杂的数据嵌入层，将原始输入转换为高维表示以增强准确性。但数据嵌入技术实际上在时间序列预测中是否有效呢？通过对十五个最先进模型和四个基准数据集进行广泛的消融研究，我们发现从许多最先进的模型中移除数据嵌入层并不会降低预测性能。在许多情况下，它反而提高了准确性和计算效率。移除嵌入层带来的收益往往超过竞争模型之间通常报告的性能差异。代码可在以下网址获取：https://github.com/neuripsdataembedidng/DataEmbedding
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
