International Research Symposium on Pure and Applied Sciences (IRSPAS)

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Author: search.filters.author.Hewaarachchi, A. P.

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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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    Periodic time series modeling for temperature data in Nuwara Eliya
    (4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Wijayawardhana, H. N. A. M.; Hewaarachchi, A. P.
    Many climatological time series display interesting properties such as trend, seasonality and autocorrelation. In addition to the autocorrelation, some time series display periodic autocorrelation, which is not readily visible. Time series, which depict periodic correlation are called as periodic time series and they can be modeled using autoregressive moving average models with periodically varying parameters. The key objective of this study was to model monthly temperature data using a periodic autoregressive model and to predict under the periodic correlation. In this research monthly mean minimum temperature series and monthly mean maximum temperature series in Nuwara Eliya district were considered. Since considered data were monthly data, the period of the data sets was twelve. In this research Fisher’s g-test was used to detect periodic correlation. Fisher’s g-test showed that only monthly mean minimum temperature series has a significant periodic correlation structure. Seasonally adjusted mean minimum temperature series was modeled using periodic autoregression (PAR) model. Using the Akaike information criterion, PAR model of order 1 with zero mean was chosen as the best fitted model for representing the seasonally adjusted data. The parameters of the model were estimated using periodic Yule-Walker estimation. Further, to check whether the periodic model was the best time series model for modeling and forecasting monthly mean minimum temperature data in Nuwara Eliya, different seasonal autoregressive integrated moving average models (SARIMA) were fitted. Finally, using forecast accuracy measurements such as Mean Error (ME), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), all models were compared and the best model was identified as the PAR(1) model that showed a ME value of -0.0861, RMSE value of 0.613 and MAPE value of 4.664%. According to comparison, periodic auto regression model of order 1 has the best forecasting accuracy among all fitted time series models
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    Joint probability distribution of daily maximum and minimum temperature data: A copula based approach
    (Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Abraj, M.; Hewaarachchi, A. P.
    The analysis of extreme daily temperature is crucial for crop production, public health development, weather predictions and decision making processes. This study examines the joint distribution of daily maximum (Tmax) and daily minimum (Tmin) temperature. For this study, daily Tmax and Tmin temperature data, measured at the Hambantota Meteorological station from January 2012 to December 2017 are used. To test the correlation, Kendall’s tau rank correlation test is used and a significant correlation (p-value < 0.05) is observed between daily Tmax and Tmin. Copula method is then used to model the dependence between Tmax and Tmin. Five candidates of univariate distributions are employed to model Tmax and Tmin separately. The parameters are estimated using the maximum likelihood method, consequently the best fitted distributions are identified based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). It is identified that Normal distribution (µ=31, 𝜎=1.5) is the best fitted univariate distribution for daily Tmax and Weibull distribution (Shape=23.3, Scale=25.6) is the best fitted univariate distribution for daily Tmin. The best fitted univariate distributions are then used to fit Frank, Clayton, Gaussian, and Gumbel Copulas. The best fitted Copula is identified based on the minimum values of AIC and BIC. To validate the best fitted Copula model, cross validation Copula Information Criterion is used. It is determined that Gaussian Copula is the best to model the dependence between daily Tmax and Tmin in Hambantota.