EDformer：用于可解释多元时间序列预测的嵌入式分解Transformer
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
AI总结：
EDformer通过嵌入式分解将多元时间序列分为季节与趋势成分，利用反向重建、注意力与前馈网络提升预测精度，并增强可解释性。
摘要：
时间序列预测是一个关键挑战，在天气预报、股票市场分析和科学模拟等领域具有重要应用。本文介绍了一种嵌入式分解Transformer——'EDformer'，用于多元时间序列预测任务。在不改变基本要素的情况下，我们重新利用了Transformer架构，并考虑了其组成部分的功能。EDformer首先将输入多元信号分解为季节性和趋势成分。接下来，显著的多元季节性成分在反向维度上重建，随后在编码器阶段应用注意力机制和前馈网络。特别是，前馈网络用于每个变量帧以学习非线性表示，而注意力机制利用嵌入在变量帧中的单个季节性序列的时间点来捕获多元相关性。因此，趋势信号通过投影添加，并执行最终预测。EDformer模型在复杂真实世界时间序列数据集上获得了最先进的预测结果，在准确性和效率方面均表现出色。本文还探讨了模型可解释性技术，以提供关于模型如何做出预测以及为什么特定特征或时间步骤重要的见解，从而增强预测结果的可解释性和可信度。
英文摘要：
Time series forecasting is a crucial challenge with significant applications in areas such as weather prediction, stock market analysis, and scientific simulations. This paper introduces an embedded decomposed transformer, 'EDformer', for multivariate time series forecasting tasks. Without altering the fundamental elements, we reuse the Transformer architecture and consider the capable functions of its constituent parts in this work. Edformer first decomposes the input multivariate signal into seasonal and trend components. Next, the prominent multivariate seasonal component is reconstructed across the reverse dimensions, followed by applying the attention mechanism and feed-forward network in the encoder stage. In particular, the feed-forward network is used for each variable frame to learn nonlinear representations, while the attention mechanism uses the time points of individual seasonal series embedded within variate frames to capture multivariate correlations. Therefore, the trend signal is added with projection and performs the final forecasting. The EDformer model obtains state-of-the-art predicting results in terms of accuracy and efficiency on complex real-world time series datasets. This paper also addresses model explainability techniques to provide insights into how the model makes its predictions and why specific features or time steps are important, enhancing the interpretability and trustworthiness of the forecasting results.
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。
