SLIIT International Conference on Advancements in Science and Humanities [SICASH]
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SLIIT International Conference on Advancements in Science and Humanities is organized by the Faculty of Humanities and Sciences of the Sri Lanka Institute of Information Technology (SLIIT), the annual research multi-conference of the faculty.
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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 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 Consumer Price Index in the United States(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Witharana, W. W. S. K; Udugama, U. K. D. T. N.; Fernando, P. M. R.,; Kaumadi, H. M. H.; Peiris, T. S. G.This report presents the Auto-Regressive Integrated Moving Average (ARIMA) model for forecasting the consumer price index (CPI) in US using monthly data from March 2010 to March 2023. The original series was not stationary, but the first difference series was found to be stationary using the Augmented Dicky Fuller test. The best-fitted model was identified based on the significance of the parameters, volatility (sigma2), log-likelihood, Akaike, Schwartz, and Hannan- Quinn information criterion. Parameters of the fitted model are significantly deviated from zero. The stability of the model has been checked using the roots of the unit root test. Residuals of the fitted model satisfied the randomness but nonconstant variance. The monthly forecasted values of CPI from April 2023 to August 2023 are 301.833, 302.444, 303.038, 303.639, and 304.261. The percentage errors of the forecasted values are less than one percent. This method and results provide useful information to policy and market makers for their planning,Publication Embargo Modeling and Forecasting of the Weekly Incidence of Dengue in Colombo District of Sri Lanka(Faculty of Humanities and Sciences, SLIIT, 2022-09-15) Arachchi, K. A. N. L. K.; Peiris, T. S. GThis study was designed to develop a time series model for the weekly incidence of dengue in the Colombo district of Sri Lanka. Weekly occurrence of dengue fever counts from January 2015 to August 2020 in the Epidemiological Report by the Ministry of Health was used for the study . ARIMA (2,1,0) with the addition of AR (16) was identified as the most effective model. The model was trained using data from January 2015 to December 2019. The balance data was used to validate the model. The residuals of the model satisfied the randomness and constant variance, but the residuals significantly deviated from the normality. The results showed that the forecasted figures were consistent with the observed series. However, a noticeable percentage error was observed sequentially in the late 2020s. Those errors could be attributable to the fact that there was an underreporting of dengue fever cases due to social and operational shocks of the Covid-19 Pandemic.