Research Publications
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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.Publication Embargo Comparison of ARIMA and LSTM in Forecasting the Retail Prices of Vegetables in Colombo, Sri Lanka(IEEE, 2022-12-09) Fonseka, D.D; Karunasena, AIdentification of vegetable price trends is important to make better decisions in the production and market. Due to several factors, including seasonality, perishability, an imbalanced supply-demand market, customer choice, and the availability of raw materials, vegetable prices fluctuate quickly and are highly unstable. In this study price prediction was concluded using two models ARIMA and LSTM with retail price data for Cabbage, Carrot, and Green beans in Colombo from 2009 to 2018. According to the decision criteria of RMSE and MAPE, the LSTM model is superior to the ARIMA model in predicting the retail prices of vegetables. There were no studies have focused on predicting prices with novel technology in the Sri Lankan vegetable market. Hence the results of this study can be used to build an advanced forecasting model by the government and decision-makers in agriculture in Sri Lanka.Publication Embargo AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance(Institute of Electrical and Electronics Engineers, 2022-10-29) Liyanage, M.L.A.P.; Hirimuthugoda, U.J; Liyanage, N.L.T.N.; Thammita, D.H.M.M.P; Koliya Harshanath Webadu Wedanage, D; Kugathasan, A; Thelijjagoda, SHigher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurrent Neural Networks (RNN), which make use of machine learning and deep learning techniques, the ultimate result has been attained. To increase application and accuracy, the newly presented technique will then be presented as a web-based software application. Contrary to what is commonly believed, this applied research proposes a new all-in-one Learning Management System (LMS) for students and tutors that acts as a central hub of all the learning resources.Publication Open Access Agro-Genius: Crop Prediction Using Machine Learning(https://ijisrt.com/agrogenius-crop-prediction-using-machine-learning, 2019-10) Gamage, M. P. A. W; Kasthurirathna, D; Paresith, M. M; Thayakaran, S; Suganya, S; Puvipavan, PThis paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.Publication Embargo CEYLAGRO: INFORMATION TECHNOLOGICAL APPROACH FOR AN OPTIMIZED AND CENTRALIZED AGRICULITURE PLATFORM(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kaushalya, T.V.H.; Wijewardana, B.Y.S.; Karunasena, A.; Kavishika, M.G.G.; Gamage, S.T.A; Weerasinghe, L.Sri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer’s vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests’ attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.