Integration Model of Weighted Fuzzy Time Series, Relative Strength Index and Ichimoku Kinko Hyo in Gold Price Forecasting (Case Study: Gold Prices for the Period April 2017 - 2023)

Gold investment presents significant profit potential but is also associated with substantial risks, making gold price forecasting a critical challenge in financial market analysis. This study integrates Weighted Fuzzy Time Series (WFTS), Relative Strength Index (RSI), and Ichimoku Kinko Hyo (IKH) to enhance the accuracy of gold price predictions. WFTS is employed to address data uncertainty by modeling price movement patterns using fuzzy logic and historical weight-based data. RSI evaluates price fluctuations over a defined period to identify overbought or oversold conditions, while IKH identifies trends and key support and resistance levels. A comparative evaluation of WFTS and ARIMA across four standard error metrics demonstrates the superior performance of WFTS in gold price forecasting accuracy. WFTS achieves lower MAE (349.55 vs 355.05), smaller MSE (186,054.98 vs 188,203.37), lower RMSE (431.34 vs 433.82), and a more favorable MAPE (19.9% vs 20.0%) than ARIMA. With reduced absolute and squared errors, WFTS proves to be a more stable and reliable predictive model, offering greater effectiveness compared to ARIMA. The results indicate that WFTS forecasts an upward trend in gold prices, providing valuable insights for investors. IKH corroborates this trend through indicators such as the Conversion Line, Base Line, Lead Line A, and Lead Line B. Additionally, RSI calculations reveal an overbought signal between 2019 and 2021, suggesting potential selling opportunities. Furthermore, the gold price remained above the lower RSI threshold, indicating a probable price increase and offering investors profitable decision-making prospects.