HTMformer: 混合时间与多变量变换器用于时间序列预测
HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
AI总结：
HTMformer通过混合时间与多变量嵌入提升时间序列预测的准确性和效率
摘要：
基于变换器的方法在时间序列预测中取得了显著成果。然而，现有的变换器仍然在序列建模方面存在局限，因为它们倾向于过度强调时间依赖性。这导致了额外的计算开销，而并未带来相应的性能提升。我们发现变换器的性能高度依赖于用于学习有效表示的嵌入方法。为了解决这个问题，我们提取多变量特征以增强嵌入层中捕捉的有效信息，从而得到多维嵌入，这些嵌入能够传达更丰富和更有意义的序列表示。这些表示使基于变换器的预测器能够更好地理解序列。具体来说，我们引入了混合时间与多变量嵌入（HTME）。HTME提取器集成了一个轻量级的时间特征提取模块和一个精心设计的多变量特征提取模块，以提供互补的特征，从而在模型复杂性和性能之间取得平衡。通过将HTME与变换器架构结合，我们提出了HTMformer，利用HTME提取器增强的特征提取能力来构建一个轻量级预测器。在八个真实世界数据集上的实验表明，我们的方法在准确性和效率方面均优于现有基线。
英文摘要：
Transformer-based methods have achieved impressive results in time series forecasting. However, existing Transformers still exhibit limitations in sequence modeling as they tend to overemphasize temporal dependencies. This incurs additional computational overhead without yielding corresponding performance gains. We find that the performance of Transformers is highly dependent on the embedding method used to learn effective representations. To address this issue, we extract multivariate features to augment the effective information captured in the embedding layer, yielding multidimensional embeddings that convey richer and more meaningful sequence representations. These representations enable Transformer-based forecasters to better understand the series. Specifically, we introduce Hybrid Temporal and Multivariate Embeddings (HTME). The HTME extractor integrates a lightweight temporal feature extraction module with a carefully designed multivariate feature extraction module to provide complementary features, thereby achieving a balance between model complexity and performance. By combining HTME with the Transformer architecture, we present HTMformer, leveraging the enhanced feature extraction capability of the HTME extractor to build a lightweight forecaster. Experiments conducted on eight real-world datasets demonstrate that our approach outperforms existing baselines in both accuracy and efficiency.
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。
