SLIIT International Conference on Advancements in Sciences and Humanities [SICASH] 2024
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/3833
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Publication Open Access Forecasting Global Annual Average CO2 Concentrations(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Rasanjali, R.P.B; Tharupathi, M.D.G; Dharmarathne, S.R.J.M; Weerakoon, M.M; Peris, T.S.GThis study aims to enhance the accuracy of CO₂ level forecasts, compare the effi cacy of diff erent predicti ve models, and provide insights for policy development. Employing ti me series and regression analysis techniques, the study uses historical data from global monitoring stati ons (1979- 2022) to model the annual mean concentrati on of atmospheric CO2 The results reveal that the ARIMA (1,1,1) model outperforms the simple linear regression model in predicti ve accuracy. Nevertheless, the regression model came across a technical problem as residuals are signifi cantly autocorrelated. The Augmented Dickey-Fuller test was applied to ensure stati onarity of the fi rst diff erence of the original series. The model was trained using data from 1979 to 2022 and validated for 2023. The errors of the ARIMA(1,1,1) was found to be white noise. The ARIMA model projected CO₂ concentrati ons of 419.5, 421.8 and 424.2 for the years 2023, 2024, and 2025 respecti vely, with a percentage error of just 0.048% for the 2023. In contrast, the corresponding percentage of error for the simple linear regression model was -1.236%. These fi ndings underscore the ARIMA model’s superior performance in forecasti ng future CO₂ levels and its suitability for environmental monitoring and climate change miti gati on strategies. This research provides a valuable methodological framework for future atmospheric science studies and informs policy decisions aimed at addressing rising CO₂ concentrations.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.