论文标题
提高变压器效率以进行多元时间序列分类
Enhancing Transformer Efficiency for Multivariate Time Series Classification
论文作者
论文摘要
大多数当前的多元时间序列（MTS）分类算法都集中在提高预测精度。但是，对于大规模（高维或长时间）时间序列（TS）数据集，还有一个附加的考虑因素：设计有效的网络体系结构以减少计算成本，例如培训时间和内存足迹。在这项工作中，我们提出了一种基于模块修剪和帕累托分析的方法，以研究模型效率与准确性之间的关系及其复杂性。基准MTS数据集的全面实验说明了我们方法的有效性。
Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequential) time series (TS) datasets, there is an additional consideration: to design an efficient network architecture to reduce computational costs such as training time and memory footprint. In this work we propose a methodology based on module-wise pruning and Pareto analysis to investigate the relationship between model efficiency and accuracy, as well as its complexity. Comprehensive experiments on benchmark MTS datasets illustrate the effectiveness of our method.
