Incorporating Taylor Series and Recursive Structure in Neural Networks
  for Time Series Prediction
Jarrod Mau Kevin Moon
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.