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Author: search.filters.author.Boongasame, LaorSubject: search.filters.subject.artificial neural network

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    Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule
    (2023-01-01)
    Boongasame, Laor
    ;
    Viriyaphol, Piboonlit
    ;
    Tassanavipas, Kriangkrai
    ;
    Temdee, Punnarumol
    Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA).