Forecasting Nonstationary Time Series Based on Dicrete Hilbert Transform - Wahyuni Ekasasmita; Khaera Tunnisa; Muh. Tri Aditya (Statistics, Optimization & Information Computing, 2024)
Various predictive methods have been applied to predict the value of stocks. The purpose of this research is to implement the discrete Hilbert transform in stock returns. The ability to predict stock price movements has big implications for investors. Traditional methods are often limited in capturing the complexity of market dynamics. It was found that the proposed method obtained an average of MAE, RMSE and MAPE values of 0.02055, 0.02237, and 0.012985 which is lower than the conventional LSTM method. This research provides a new understanding of the application of discrete Hilbert transform in a dynamic global financial context.