Forecasting For Silver Closing Price and Modifying Predictions by Using Wavelet Transformation
DOI:
https://doi.org/10.25156/ptjhss.v3n1y2022.pp129-142Keywords:
Time Series Analysis, ARIMA model, Forecasting, wavelet transformationAbstract
Silver is a precious metal and the spot price not only reflects the current supply and demand condition but it also reflects investors’ expectations of future inflation and other general business/economic conditions. Therefore, the main objective of this study is to forecasting yearly silver closing price and modifying the prediction by using wavelet transformation. This study aims to analyze the time series of yearly silver closing price for the period between (1969 to 2022) using time series analysis which is (Box-Jenkins) method for the accuracy and flexibility it has in addition modifying the prediction by using wavelet transformation. In this study. The study found that the fit and efficient model according to smallest measurements (RMSE, MAPE, MAE, and ME) is the ARIMA(2, 2, 1) model. According to the results of ARIMA(2, 2, 1), the amounts of yearly silver closing price have been modified by wavelet transformation which has smallest RMSE and ME when compared with the original ARIMA(2, 2, 1).
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Copyright (c) 2022 Shaima N.Yaequb; Azhy A. Aziz; Huda M. Saeed; Heshu O. Faqe
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