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Item MODELING COVID-19 CASES IN SRI LANKA USING ARIMA MODELS(The Open University Of Srilanka., 2020) Attanayake, A.M.C.H.; Perera, S.S.N.COVID-19 (Novel Coronavirus) is a pandemic which spread around the world at an alarming rate. As of 10th June 2020, 1,880 infections and 11 deaths were reported in Sri Lanka due to COVID-19. The number of infections increase day by day requiring research on modelling the pandemic. Modelling of COVID 19 cases will be useful to understand the behavioural patterns of the disease and hence to identify control mechanisms. The aim of this study is to model and predict the daily cumulative COVID-19 cases in Sri Lanka. Autoregressive Integrated Moving Average (ARIMA) technique was applied to model the reported COVID-19 cases in Sri Lanka. Data from 11th March - 1st of June 2020 were used for the model development and data from 2nd - 10th June 2020 (10% of data) were used for model validation. In the analysis, second order differencing removed the non-stationarity of the original series. Different candidate ARIMA models were tested based on ACF and PACF plots and the best ARIMA model was selected based on minimum AIC and BIC measures. The most appropriate ARIMA model for the COVID-19 cases in Sri Lanka is ARIMA (2,2,2). After verifying the assumptions of the model, MAPE of the validation set revealed 1.86%. Therefore, the selected most appropriate model was used to forecast the future COVID-19 cases in Sri Lanka. According to the forecasted values of the model, it can be concluded that COVID19 cases in Sri Lanka will increase slowly in the upcoming days. ARIMA technique is appropriate in only short-term forecasting. Availability of an effective prediction model will be helpful in anticipating the cases and to take timely action to control the COVID-19 incidence. Unexpected recordings cannot be modelled and predicted by the fitted models. Uncertainties limit the effectiveness of a model, specially, in an epidemic like novel coronavirus.Item COMPARISON OF PERFORMANCES OFSELECTED FORECASTING MODELS:AN APPLICATION TO DENGUE DATA IN COLOMBO, SRI LANKA(Department of Statistics & Computer Science, Faculty of Science,& Research & Development Centre for Mathematical Modelling, Faculty of Science, University of Colombo, Sri Lanka., 2021) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.Dengue is a one of the diseases in the world which has no exact treatment to recover from the disease. It is rapidly spreading throughout the world by causing large number of deaths [1]. In Sri Lanka, there is an increase of reported dengue cases over recent years. The majority of dengue cases reported in the Colombo district within the Sri Lanka. Effective dengue management and controlling strategies should be implemented to reduce the deaths from the disease. Modelling and predicting the distribution of the dengue will be useful in detecting outbreaks of the dengue and to execute controlling actions beforehand. The objective of this study is to develop an appropriate modelling technique to predict dengue cases. To accomplish this objective, we have chosen our study area as Colombo, Sri Lanka. Seven modelling techniques, namely, Na¨ıve, Seasonal Na¨ıve, Random Walk with Drift, Mean Forecasting, Autoregressive Integrated Moving Average, Exponential Smoothing and TBATS (Trigonometric, Box-Cox Transformation, ARMA errors, Trend and Seasonal components) [2] were chosen in this study to model dengue data. For model development process, monthly reported dengue cases in Colombo from January 2010 to December 2018 were used and validated using the data from January to December in 2019. Mean error, root mean squared error and mean absolute percentage error measurements were used to select the most parsimonious model to predict dengue cases in Colombo, Sri Lanka. Both Exponential and TBATS models were competed in predicting dengue cases by reporting minimum error measures. Therefore, results disclosed that among the selected methods either Exponential Smoothing model or TBATS model can be used to predict dengue cases in Colombo, Sri Lanka.Item 2-Tuple Fuzzy Linguistic Model to Evaluate the Risk of Invasive Plant Species(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Peiris, H.O.W.; Perera, S.S.N.; Chakraverty, S.; Ranwala, S.M.W.Management of invasive species can appear to be a complicated and unending task. In order to manage the spread, these species need to be undergone any risk assessment during their introduction. The aim of this study is to evaluate the aggregate risk of Invasive Alien Species (IAS) using invasive attributes. We use the 2-tuple fuzzy linguistic representation to develop the model without loss of information in which occur in ordinary linguistic operators. These risk values are compared with the National Risk assessment scores which are in the form of Linguistic labels. The proposed model is validated using few known noninvasive species in Sri Lanka. The model gives significant predictions and it is found to be a better tracking system for identifying potential invaders than the conventional risk assessment methods.Item Analysis of Factors Affecting USD/LKR Exchange Rate(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Jayasuriya, D.P.S.H.; Perera, S.S.N.This paper intends to investigate the factors affecting the US Dollar exchange rate in Sri Lanka, in the period of January 2009 to June 2015, by using the econometric framework of Johanson and Juselius Cointegration, Vector Auto Regressive model, Granger Causality, and Variance Decomposition analysis. The empirical results of the model indicate that the increase in previous month net foreign assets and trade balance, and a decrease in the previous month exchange rate, has a significant influence on the short run appreciation of exchange rate. Granger Causality test confirms past values of net foreign assets, trade balance, and workers’ remittance have a predictive ability in determining the present values of exchange rate while, Variance Decomposition indicate, variation in exchange rate in short term and long term time horizon is due to the exchange rate itself and net foreign assets, trade balance and workers’ remittance respectively.