BBRI STOCK PRICE FORECASTING USING THE HOLT-WINTERS DOUBLE EXPONENTIAL SMOOTHING METHOD
DOI:
https://doi.org/10.32477/semnas.v4i1.1319Keywords:
Stock Price Forecasting, Time Series, Exponentian Smoothing, Bank Rakyat Indonesia (BRI).Abstract
The Holt-Winters Double Exponential Smoothing method is a forecasting method that has two parameters, namely α and β. This method is used when the data shows a trend and seasonal pattern. This study applies this method to forecast BBRI's share price as part of a time series analysis. The dataset used is the adjusted close price of BBRI shares. The results showed that the optimal forecasting model used α (alpha) of 0.4 and β (beta) of 0.1 with an estimated α smoothing parameter of 89.3348466 and β of -0.3743472. The model evaluation was assessed based on the measures of forecasting errors in the form of SMAPE and MAPE as the main error evaluation measurements and SSE, MSE, and RMSE as supporting error evaluation measurements.
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