International Research Symposium on Pure and Applied Sciences (IRSPAS)

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    Stable forecasting of tax revenues of selected countries assisted by Clustering Approach
    (4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Karunarathne, A. W. S. P.; Liyanage, U. P.; Hewaarachchi, A. P.
    Tax is one of the main income of a government that utilizes in public welfare and future investment. Taxation has goals: reducing the inequalities through a policy of redistribution of income, administrating the levels of inflation as well as deflation, protecting the local industries from foreign competitions through levies, and discouraging the undesirable activities such as consumption of tobacco. Additionally, taxation provides a major portion of Gross Domestic Product (GDP), depending on the country’s fiscal policy. Tax forecasting is essential towards strategizing government plans and future activities. However, tax revenue highly fluctuates due to many factors which include natural disasters, instability of political environment and government monitory policies. This study aims to find the set of best statistical forecasting models, by comparing the behavioral similarities of different tax revenues identified by clustering approach. Here, tax revenue data from 1972 to 2017 of 24 countries belonging to developing status: developed, developing and under-developed have been analyzed. Comparable and homogenize measure is obtained considering the tax revenue as a percentage of GDP. The countries with similar tax revenue are identified by using K-Means clustering. Consequently, the selected countries were clustered into five classes depending on their tax revenue as a percentage of GDP. The analysis shows that the tax revenue has similar behavior based on the similarities of countries’ developing status. Tax revenues data in each cluster were analyzed to identify the best fitted time series models. It has been found that models of the types Autoregressive Moving Average (ARMA) and Autoregressive (AR) are best fitted models for the representing tax revenue of the corresponding clusters. As an example, ARMA (2,2) model was fitted to one cluster and AR (1) model was fitted for another cluster of countries. According to the type of the model and their range of parameter values, it is found that similar models can be used to represent the tax revenue data within the underlying cluster. That is, there exist cluster specific models in the sense of model type and their parameter ranges. This finding can be utilized towards forecasting tax revenue in the case of the revenue data are highly affected with a qualitative factor, for example, political instability. In summary, through the clustering approach, stable forecasting of revenue data of a given country can be performed.
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    Categorizing T20 batsmen based on their performances
    (Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Sumithrapala, D. M. S. S.; Mallawa Arachchi, D. K.
    T20 Cricket is the most popular shortest form of cricket which is played all over the world. It is obvious that some of the T20 batsmen are power hitters. Some of them are having higher averages and higher strike rates. The aim is to categorize the batsmen who are showing similar performances. In this research work, we perform a statistical analysis to categorize batsmen in the world based on their performances shown during the T20 matches that they have played. Several factors have been considered in our analysis namely, highest score scored by a batsmen, average, strike rate, number of 4s scored, number of 6s scored and number of half centuries scored. Cluster analysis was used in determining the number of clusters into which the batsmen should be clustered. This study helps identifying the batsmen who are showing the highest performances in T20 cricket. It enables one to predict the range of runs scored for the batting inning if a team is set with these batsmen and also it is useful to rank the batsmen. Data were collected through Cricinfo website from 58 T20 batsmen throughout the world. Analysis was done to identify the relatively homogeneous clusters, using Ward’s method of Hierarchical Cluster Analysis using SPSS statistical software and R-Studio. When considering the performances shown by the batsmen, there is no enough evidence to conclude that the batsmen who are showing the highest performances belong to one country or continent. When all the variables are considered together, Mohammad Shahzad, MJ Guptil, BB Macullum, TM Dilshan, and DPMD Jayawardene can be categorized as the batsmen showing the best performances in T20 while CH Gayle, MJ Guptil, BB Macullum, MEK Hussey, Najibullah Zadran, KP Pietersen, F du Plessis have both the highest batting averages and strike rates. It can be concluded that MJ Guptil and BB Macullum are the two batsmen showing the best performances in all forms of T20 cricket. The research helps to identify how cluster changes with different factors.