Forecasting Global Silver Prices using Autoregressive Integrated Moving Average Time Series Models
DOI:
https://doi.org/10.21928/uhdjst.v10n1y2026.pp165-174Keywords:
Silver Price Forecasting, Autoregressive Integrated Moving Average Model, Time Series Analysis, Commodity Prices, Financial Forecasting, Global Silver MarketAbstract
This study examines the application of the autoregressive (AR) integrated moving average (ARIMA) model can be used to forecast global silver prices expressed in US dollars. Daily silver price data from 1 January 2025 to 10 March 2026 were used to identify the most appropriate time series model using the Box-Jenkins methodology to be used in accurate forecasting. The determination of an appropriate ARIMA model, using AR and moving average orders from 0 to 2, was made by evaluating a number of different candidates using both the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for model selection purposes. The ARIMA (2,1,1) specification had the lowest AIC and BIC among all specifications evaluated, thus being selected as the most appropriate ARIMA model. The ACF and PACF plots confirmed that the residuals do not have any significant autocorrection and residues that are not normally distributed. The results of the forecasting are very useful in terms of making decisions and planning strategies because they allow investors and enterprises to predict upcoming changes in silver prices and reduce financial risks. In general, this analysis shows that the result indicates that the ARIMA model provides reliable forecasts for silver price prediction.
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