Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction

Time series analysis is relevant in various disciplines such as physics, biology, chemistry, and Time series analysis plays a pivotal role in extracting valuable finance. In this paper, we present a novel neural insights from sequential data, uncovering patterns, network architecture that integrates elements trends, and underlying structures that drive temporal dynamics from ResNet structures, while introducing the innovative (Zhang, 2003; Tang et al., 1991). The ubiquity incorporation of the Taylor series framework. of time series data across diverse domains, including finance, This approach demonstrates notable enhancements healthcare, and environmental science, underscores in test accuracy across many of the the critical need for accurate and efficient analytical methods baseline datasets investigated.