Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule

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dc.contributor.authorBoongasame, Laor
dc.contributor.authorViriyaphol, Piboonlit
dc.contributor.authorTassanavipas, Kriangkrai
dc.contributor.authorTemdee, Punnarumol
dc.date.accessioned2026-08-06T10:40:38Z
dc.date.available2026-08-06T10:40:38Z
dc.date.issued2023-01-01
dc.description.abstractSince 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).
dc.identifier.citationJournal of Mobile Multimedia, 19(1), 165-186, 2023
dc.identifier.doi10.13052/jmm1550-4646.1919
dc.identifier.issn15504646
dc.identifier.other2-s2.0-85139754957
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14196
dc.sourceJournal of Mobile Multimedia
dc.subjectartificial neural network
dc.subjectassociation rule
dc.subjectgold forecasting
dc.subjectLSTM
dc.titleGold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule
dc.typeArticle

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