Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms

This thesis presents a novel method for improving time series data preprocessing by incorporating a cycling layer into self-attention mechanisms. Traditional techniques often struggle to capture the cyclical nature of time series data, impacting predictive model accuracy. By integrating a cycling layer, this thesis aims to enhance the ability of models to recognize and utilize cyclical patterns within datasets, exemplified by the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results demonstrate that the proposed method not only improves the accuracy of forecasts but also increases model fitting speed compared to conventional approaches. This thesis contributes to the advancement of time series analysis by offering a more effective preprocessing technique.