Feature selection is a known technique to preprocess the data before performing any data mining task. In multivariate time series (MTS) prediction, feature selection needs to find both the most related variables and their corresponding delays. Both aspects, to a certain extent, represent essential characteristics of system dynamics. However, the variable and delay selection for MTS is a challenging task when the system is nonlinear and noisy. In this paper, a multiattention-based supervised feature selection method is proposed. It translates the feature weight generation problem into a bidirectional attention generation problem with two parallel placed attention modules. The input 2D data are sliced into 1D data from two orthogonal directions, and each attention module generates attention weights from their respective dimensions. To facilitate the feature selection from the global perspective, we proposed a global weight generation method that calculates a dot product operation on the weight values of the two dimensions. To avoid the disturbance of attention weights due to noise and duplicated features, the final feature weight matrix is calculated based on the statistics of the entire training set. Experimental results show that this proposed method achieves the best performance on compared synthesized, small, medium, and practical industrial datasets, compared to several state-of-the-art baseline feature selection methods.

中文翻译：

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一种基于多注意力监督的多元时间序列特征选择方法

特征选择是一种在执行任何数据挖掘任务之前对数据进行预处理的已知技术。在多元时间序列 (MTS) 预测中，特征选择需要找到最相关的变量及其相应的延迟。这两个方面在一定程度上代表了系统动力学的本质特征。然而，当系统是非线性和嘈杂时，MTS 的变量和延迟选择是一项具有挑战性的任务。在本文中，提出了一种基于多注意力的监督特征选择方法。它将特征权重生成问题转化为具有两个平行放置的注意力模块的双向注意力生成问题。将输入的二维数据从两个正交方向切分为一维数据，每个注意力模块从各自的维度生成注意力权重。为了便于从全局角度进行特征选择，我们提出了一种全局权重生成方法，该方法对两个维度的权重值进行点积运算。为了避免噪声和重复特征对注意力权重的干扰，最终的特征权重矩阵是根据整个训练集的统计数据计算的。实验结果表明，与几种最先进的基线特征选择方法相比，该方法在比较合成的、小型、中型和实用的工业数据集上取得了最佳性能。为了避免噪声和重复特征对注意力权重的干扰，最终的特征权重矩阵是根据整个训练集的统计数据计算的。实验结果表明，与几种最先进的基线特征选择方法相比，该方法在比较合成的、小型、中型和实用的工业数据集上取得了最佳性能。为了避免噪声和重复特征对注意力权重的干扰，最终的特征权重矩阵是根据整个训练集的统计数据计算的。实验结果表明，与几种最先进的基线特征选择方法相比，该方法在比较合成的、小型、中型和实用的工业数据集上取得了最佳性能。