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 Time Series Model to Predict the Weekly Percentage of Python Programming Language usage(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Gunawardane, D. M. N. M.; Herath, H. M. P. T.; Pitiyekumbura, W. S.; Samodhika, P. L. D.; Athauda, A. M. B. T.; Amarasinghe,E. J. C. U.; Peiris, T. S. G.Python's super popular and getting bigger fast. Figuring out how it will be used is super important for planning what to teach, training tech workers, and making good rules, especially in places like Sri Lanka that are just now getting into digital stuff. Therefore, this study aims to predict the weekly global usage of Python. We looked at data from April 21, 2019, to April 21, 2024. We got 262 weeks. This data is entered into Kaggle from Google search interest scores (Nextmillionaire, 2023). This dataset shows the highest interest score for Python in the general world. After trying out a bunch of models, theARIMA (1,1,1) model with seasonal stuff seemed like the best fit. We taught the model with data from April 21, 2019, to January 28, 2024 (250 weeks) and checked it with data from February 4, 2024, to April 21, 2024 (12 weeks). We tested the model to make sure it was doing things right, and the leftovers looked random, which is a good thing. The MAPE (Mean Absolute Percentage Error) for the validation data is 6.04%. This shows the ARIMA model is pretty good at guessing Python usage over time. Because theguesses are pretty accurate and consistent, it looks like Python usage of global is going up steadily. This means Python is a big deal with both Data Science & Analytics, Machine Learning & AI, Cloud Computing & DevOps, Automation & Scripting. This info should help schools, training places, and the government make smart choices about teaching digital skills.Publication Open Access Predictive Model for the SPDR S&P 500 ETF (SPY) using Volatility Analysis Approach(Department of Mathematics and Statistics, Faculty of Humanities and Sciences,SLIIT, 2025-10-10) Musharraff, N. I.; Fernando, W. S. C.; Godage, T. R.; Jayasooriya, J. M. T. S.; Siriwardhana, H. A. A. T. P.; Samasundara, T. A.; Guruge, M. L.; Peiris, T. S. G.The S&P 500 (Standard & poor’s 500) is one of the most widely followed equity indices in the world. The SPDR S&P 500 ETF Trust (SPY) is used to track the performance of the S&P 500 index as closely as possible and can also be traded in the stock exchanges. Not many studies have been carried out to forecast daily closing prices of SPY for recent years. This study presents a time series analysis and forecasting of the daily closing prices of the SPY index. The dataset extends from 2000 to 2025, capturing key financial events, market movements and long-term growth trends. Due to high volatility, we were forced to consider variance equation in additional to the mean equation and the best fitted model identifies is ARIMA (1,1,1) + GARCH (1,1).ARIMAPublication 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 Model Comparison to Forecast Gross Domestic Product (GDP) in China(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Koswaththa, N.B.K.S.M.; Gaganathara, G.A.G.D.; Fernando, A.S.M.S; Dissanayake, M.D.T.G.; Guruge, M. L.Gross Domestic Product (GDP) is an accurate indicator to measure the size of the economic performance of a country and its growth rate. This study focuses on finding a suitable model to forecast GDP in China, which is one of the world’s largest and most rapidly developing economies. A simple linear regression model with AR(1) error structure and Autoregressive Integrated Moving Average (ARIMA) model were developed and compared for the purpose. A secondary data set which includes GDP in China from 1952 to 2020 was used for this study and the sample size was 69. Residual diagnostics tests were conducted to check the assumptions and model adequacy of each model. It was found that out of the fitted models, ARIMA (1,1,1) is the most appropriate model to forecast GDP in China as it gave lower MAE and RMSE compared to fitted simple linear regression model with AR(1) error structure. Model comparison was done using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The predicted values for 2023, 2024 and 2025 are 1436349, 1447149 and 1457950 respectively. E-views 8.0 and Minitab software were used to analyze the data.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.