Time series analysis is relevant in various disciplines such as physics,
biology, chemistry, and finance. In this paper, we present a novel neural
network architecture that integrates elements from ResNet structures, while
introducing the innovative incorporation of the Taylor series framework. This
approach demonstrates notable enhancements in test accuracy across many of the
baseline datasets investigated. Furthermore, we extend our method to
incorporate a recursive step, which leads to even further improvements in test
accuracy. Our findings underscore the potential of our proposed model to
significantly advance time series analysis methodologies, offering promising
avenues for future research and application.

本文介绍了一种新颖的神经网络架构，该架构整合了 ResNet 结构的元素，并引入了 Taylor 级数框架的创新融合。这种方法在许多基准数据集上展现出显著的测试准确性改进，并进一步通过引入递归步骤来提高测试准确性。我们的研究结果强调了我们提出的模型在显著推进时间序列分析方法学方面的潜力，为未来的研究和应用提供了有希望的途径。