Forecasting Nonstationary Time Series Based on Dicrete Hilbert Transform

  • Wahyuni Ekasasmita Department of Mathematics, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
  • Khaera Tunnisa Department of Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
  • Muh. Tri Aditya Department of Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
DOI: https://doi.org/10.19139/soic-2310-5070-2060
Keywords: Hilbert Transform, Forecasting, Time Series, Machine Learning

Abstract

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.
Published
2024-08-04
How to Cite
Ekasasmita, W., Tunnisa, K., & Aditya, M. T. (2024). Forecasting Nonstationary Time Series Based on Dicrete Hilbert Transform. Statistics, Optimization & Information Computing, 13(1), 368-377. https://doi.org/10.19139/soic-2310-5070-2060
Issue
Vol 13 No 1 (2025)
Section
Research Articles
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