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All Journal Jurnal Sarjana Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jambura Journal of Mathematics Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Applied Engineering and Technological Science (JAETS) Journal of Computer Scine and Information Technology SATIN - Sains dan Teknologi Informasi
Gushelmi
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics

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Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices Bagus Priambodo; Ruci Meiyanti; Samidi Samidi; Gushelmi Gushelmi; Rabiah Abdul Kadir; Azlina Ahmad
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6073

Abstract

The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score and lower RMSE score showing that Fibonacci levels influence the accuracy of gold price predictions and strengthen the overall reliability of gold price forecasts. The findings underscore the potential of combining machine learning models with technical analysis tools in financial forecasting. Integrating the Fibonacci retracement level offers valuable insights for market participants, enabling more informed investment decisions and effective risk management strategies.
Co-Authors Atika Ratna Dewi Azlina Ahmad Bagus Priambodo Dinda Putri Insani Dodi Guswandi emil nafan Emil Naf’an Emil Naf’an Fajrul Islami Fauzan Ramadhoni Febri Hadi Fenia, Selvi Zola Liga Mayola M.Hafizh Maifuza Binti Mohd Amin Miftahul Huda Mondra Neldi Rabiah Abdul Kadir Ridho Ananda Ruci Meiyanti Samidi Samidi Triana Novita Yayan Septiadi