SLIIT Conference and Symposium Proceedings
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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Open Access Development of an ARIMA Model to Predict the Monthly Price of Bitcoin in USD(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Kapukotuwa, R.W.M.C.L.B.; Muthuranwela, M.M.P.L.; Samarakoon, H.G.I.L.; Dilshan, P.G.S.; Sajeewani, A.K.R.K.; Peiris, T. S. G.This study examines the bitcoin price in USD in the world by developing a suitable ti me series model to identi fy its future trends. This data set consists of monthly bitcoin prices from August 2010 to July 2024. It was found that the original series is not stati onary and not seasonality. The stati onary was achieved by the fi rst diff erence. Of the parsimonious models identi fi ed based on the Parti al Autocorrelati on Functi on (PACF) and Autocorrelati on Functi on (ACF) of the stati onary series, an auto-regressive integrated moving average (ARIMA) (2,1,2) model was identi fi ed as the best-fi tt ed model. The signifi cance of the model and its parameters and informati on criteria such as the Akaike Informati on Criterion (AIC), Schwarz Criterion, and log-likelihood was used to identi fy the best-fi tt ed model. The model was trained using data from August 2010 to March 2024. The residuals of the model were found to be white noise. The mean absolute percentage error (MAPE) for validati on data is 7.09%. The percentage errors for the validati ng set are all positi ve and varied from 3.5% to 12.9%. The predicted Bitcoin price (USD) from August to October 2024 are $59947.88, $60308.7, and $60669.53. Bitcoin price can be uti lized by market demand and supply, regulatory environment, and technology development.Publication Open Access Development of Time Series Model to Predict Daily Gold Price(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Gayashan, W. A. K.; Dayarathna, A. K. G.; Rajakaruna, R. W. M. A. P.; Perera, T. J. N.; Peiris, T. S. G.Gold is ancient and one of the most precious and popular commoditi es in the world. Gold price forecasti ng is criti cal in fi nancial decision-making, providing valuable informati on for investors in the gold market, sellers of gold items and stakeholders. Not much studies have been carried out in to forecast daily gold prices of Sri Lanka. The aim of this paper is to forecast the daily gold price rate (Rupees/troy ounce) using data from 2nd January 2018 to 14th June 2024 published by the Central Bank of Sri Lanka. The best fi tt ed model was identi fi ed as ARIMA (1,1,1) + ARCH (2). The model was trained using data from 2nd January 2018 to 31st May 2024 and validated using data from the 3rd of June 2024 to 14th of June 2024. The model was stati sti cally tested using standard stati sti cal procedure and errors were found as white noise. The Mean Absolute Percentage Error (MAPE) for the training data set and validati on data set were 0.748% and 1.002% respecti vely. The validati on confi rmed that the ARIMA (1,1,1) + ARCH (2) model eff ecti vely captures the dynamics of gold price movements, off ering robust predicti ve power. These results indicate that the model is highly accurate and reliable for forecasti ng, making it a valuable tool for fi nancial insti tuti ons and investors aiming to predict market trends and make informed investment decisions.