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Item Studying the behaviour of export quantities of Tuna fish in Sri Lanka(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Sachithra, S. A. L.; Liyanage, U. P.; Wijeyaratne, W. M. D. N.Being an island in the Indian Ocean, Sri Lanka claims a large sea area and abundant fish resource with high facilitate suitable for large scale fishery industry. According to the Central Bank of Sri Lanka, the contribution of fisheries to the Gross Domestic Production (GDP) of the country ranges between 1.3% and 1.6%. Consequently, fishery industry already plays a vital role in economics and social development of Sri Lanka. Due to weather conditions, seasonal effects, changes of government tax policies and trade agreements, e.g. GSP+ and etc., there is a high fluctuation in export quantity of fishery products in Sri Lanka. Thereby, it is essential to study the variation patterns and forecast harvest and income generated by fishery products towards monitory strategy planning. Among the various types of fish, tuna is one of the species that is important in financial earnings. Out of all fisheries exports, Sri Lanka earns the highest income worth 50.8% by exporting tuna fish in 2016, according to the statistics from Ministry of Fisheries and Aquatic Development of Sri Lanka (SLMFAD). This study was conducted to analyze the export quantities of tuna fish and forecast the future export quantities. Monthly export quantities from January, 2010 to June, 2018 were collected from SLMFAD. In preliminary analysis, United States, Japan, and Canada are identified as the top countries in which Sri Lanka exports the highest quantity of tuna fish. To study the changes in export patterns and their associated relations, Statistical Change-Point Analysis was conducted. The results revealed a high correlation between the changes of export patterns with events such as country’s peace restoration, economic stability, infrastructure facilities, introduction of different capacity changes and termination of development projects. Towards forecasting the export patterns time series data analysis techniques were used. Unit root tests; Augmented-Dickey-Fuller Test (ADF) and Kwiatkowski-Phillips-Schmidt-Shin test (KPSS) were used to test the stationarity of the time series data. Based on Akaike information criterion (AIC) value, SARIMA (1,1,2)(1,0,0)12 model was identified as the best. Ljung-Box test, Jarque-Bera test and Heteroscedasticsity test were used to check the behavior of the residuals of this fitted models. Accuracy of the models were compared by root mean squared error (RMSE), and mean squared error (MSE). With 0.8485 of RMSE and 0.6038 of MSE, SARIMA (1,1,2)(1,0,0)12 model can be considered as the most suitable model to forecast the export tuna quantity from Sri Lanka.Item Time series modeling of red onion production in Jaffna, Sri Lanka(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Mirojan, U.; Varathan, N.; Arumairajan, S.Onion is one of the most important commercial vegetable crops grown in Sri Lanka. Observing fluctuation of onion production is essential in the market economy. The level of the production and the fluctuation not only has a significant influence on farmers and consumers, but also a reasonable effect on the safe running of the onion in market. In this study, the annual production of red onion in Jaffna is modeled by using Box – Jenkins time series approach. The Onion production in Jaffna is cultivated in two seasons, Maha season: from September to March, Yala season: from April to August. The annual seasonal red onion production data was obtained from the office of the Deputy Provincial Director of Agriculture (Extension) during the period of 1987 to 2016. The main objective of this study is to find the suitable Auto Regressive Integrated Moving Average (ARIMA) model for the annual production of Red onion in Jaffna. Further, three statistical criteria such as Akaike’s information criteria, Bayesian information criteria, mean squared error were carried out in order to select the best ARIMA model. Through the modeling, it was identified that ARIMA (1,1,0) is the best fitting model to the given data. Moreover, the model validation has been done using the actual figures. Further, the identified best model can be used to predict the red onion production of Jaffna in near future.Item Statistical modelling of monthly electricity sales in Colombo: ARIMA approach(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Herath, H. M. R. D. S.; Varathan, N.Electricity is the most essential form of energy, used all over the globe. It has influenced the economy, public health, technological growth and all other spheres of human activities. The electricity sales are growing day by day with the population growth and industrialization, etc. Even though Sri Lanka is a developing country, it has shown a huge progress, showing a national electrification ratio of 99.7% in 2017. Colombo; the capital of Sri Lanka, is the main commercial hub with the largest population and by far the most developed city in Sri Lanka. This study investigates to develop a suitable time series model for the monthly electricity sales of Colombo City. The monthly electricity sales data was obtained from Ceylon Electricity Board during the period of January 1982 to December 2016. The data analysis has been done using the Box-Jenkin’s Auto Regressive Integrated Moving Average (ARIMA) procedure. Results reveal that, the SARIMA (0,1,2)(0,1,1)12 is the most appropriate model for the monthly electricity sales data. Moreover, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mean Square Error (MSE) were used to select the best model. Further, adequacy of the best model has been checked using Ljung-Box Chi-Squared test. Finally, the monthly electricity sales for the year 2017 were predicted using the selected best model.Item Modelling and Forecasting Tourist Arrivals in Sri Lanka(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Priyangika, J.H.; Pallawala, P.K.B.N.M.; Sooriyaarachchi, D.J.C.Tourism is one of the major industry shows a rapid growth in the Sri Lankan economy. According to the annual tourism statistics, the international tourist arrivals shows 4.4% growth in 2015 and 27.72% growth in foreign exchange earnings in the same year compared to 2014. Therefore, understanding and examining the upcoming trends of tourist’s arrivals is really important and it will be beneficial and important for stakeholders and interesting parties of the country. The purpose of this research study is to investigate and forecast the tourist’s arrival in Sri Lanka based on the available past data. The collected tourist’s arrivals data from 2000 January to 2014 December are used for this study. For the reason that the tourist’s arrivals data follow univariate time series, the time series techniques, ARIMA and GARCH models, are proposed to use in forecasting. Since the data consists with heteroscedasticity, transformation methods are needed to use in some time series modelling approaches. In GARCH model approach, original data is used for identifying a suitable model while Box-cox transform is used in SARIMA model approach to overcome the heteroscedasticity problem. Basically, model selection is done based on AIC values and MAPE, MAE and RMSE values used for measure the performance of selected models. Among the proposed time series models, none of the SARIMA models are fitted well for the data as they are not diagnostic. Finally, ARCH (1) model with optimal lag (2, 7, and 12) is identified as the best model to forecast the future values of tourist’s arrivals in Sri Lanka.Item A New Financial Time Series Approach for Volatility Forecasting(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Rathnayaka, R.M.K.T.; Seneiratna, D.M.K.N.; Arumawadu, H.I.The investment in capital market is easiest, fastest and securable way for building healthy financial foundation today. Because of the economic outlooks causing directly on these market fluctuations, the making decisions in the equity market has been regarding as one of the biggest challenges in the modern economy. The main purpose of this study is to take an attempt to understand the behavioral patterns and seek to develop a new hybrid forecasting approach under the volatility. The results are successfully implemented on Colombo stock exchange (CSE), Sri Lanka over the three year period from January 2013 to December 2015. The empirical results indicated that the new proposed hybrid approach is more suitable for forecasting price indices than traditional time series forecasting methodologies under the high volatility.