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.Publication Open Access Forecasting the Monthly Real Wage Rate of the Public Sector in Sri Lanka(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Gamage, S.K.A.; Gunasekara, K.L.R.K.; Gamage, P.S.H.; Ariyarathna, H.B.M.S.; De Mel, W.N.R.; Peiris, T.S.G.This study assesses the monthly real wage rate of the public sector of Sri Lanka by elaborati ng a suitable ti me series model to identi fy the future trends associated with the real wage rates of Sri Lanka. The sample data set consists of monthly real wage rate data from January 2018 to March 2024 from the Central Bank of Sri Lanka (CBSL). The real wage rate has been calculated selecti ng 2016 as the base year. Suitable parsimonious models were identi fi ed through the patt erns of the sample parti al autocorrelati on functi on (PACF) and sample auto-correlati on functi on (ACF) of the stati onary series. Based on the indicati ons such as Akaike informati on criterion (AIC), Schwarz Criterion (SC) and log likelihood an autoregressive integrated moving average (ARIMA) model of the type (0,1,2) was disti nguished as the best fi tt ed model. The residuals of the best fi tt ed model were ascertained to be white noise. The model has been validated for the fi rst three months of 2024. The Mean Absolute Percentage Error (MAPE) for the validati on data is 9.59%. The forecasted wage rate values from April 2024 to September 2024 are 54.562, 54.096, 53.631, 53.165, 52.7 and 52.234 respecti vely. The study’s fi ndings can be uti lized by policymakers, economists, and government workers to improve their fi nancial planning.Publication Open Access Forecasting of Constant GDP per capita of Sri Lanka using ARIMA model(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Wijesinghe, W.R.A.N.M; Umayangi, K.A.S,; De Silva, H.D.K; Mundigalage, S.D.M; Peiris, T. S. GGDP per capita is a global measurement for assessing the economic prosperity of nations. Constant (Real) GDP per capita eliminates the effects of inflation which allows for a more accurate comparison of GDP per capita over time. However, no statistical models have been developed to predict annual constant GDP per capita (CGDPC) in Sri Lanka. In this study, ARIMA (1,1,0) model was developed using past data from 1961 to 2018 to forecast CGDPC. The best-fitted model was identified based on three possible models using sample ACF and sample PACF of the stationary series and comparing statistics such as AIC, BIC, maximum log-likelihood, and volatility. The residuals of the fitted model were white noise. The training dataset has percentage errors ranging from -6.50% to 3.80%. The model was validated for observed data in 2019, 2020, and 2021. The percentage error for the three points were -3.49, -6.10, and 1.49 respectively. The forecasted values for 2022, 2023, and 2024 obtained were 4506.728, 4653.895, and 4810.505 respectively showing that Sri Lanka’s economy is expected to grow due to the increase in CGDPC. The GDP per capita growth rates of 2.99%, 3.27%, and 3.37% for the next 3 years also confirm this. The results obtained from this study can be effectively used for better planning. However, it is recommended to improve the model further to reduce the percentage of errors using the ARIMAX approach.Publication Embargo Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dassanayake, W.; Ardekani, I.; Gamage, N.; Jayawardena, C.; Sharifzadeh, H.Time series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.