Browsing by Author "Hewaarachchi, A. P."

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Analysing the Factors Affecting to Women’s Engagement in Sri Lankan Labor Force
(Faculty of Commerce and Management Studies University of Kelaniya., 2024-11-01) Nanayakkara, N. W. H. G. K. K.; Hewaarachchi, A. P.; Kavinga, H. W. B.; Wijebandara, W. A. C.
Women's engagement in the workforce is a key factor in driving economic growth in Sri Lanka. Despite the country's advancements in recent years, women still face significant challenges that prevent them from fully participating in the workforce. The objective of this study is to explores the potential labor force of women in Sri Lanka and the factors affecting their participation, using data from the Labor Force Survey 2021. Analyzing data from 41,171 women out of 77,869 individuals using a binary logistic regression model, the study considered factors such as marital status, education level, age group, relationship to the head of the household, district, sector, ethnic group, religion, Sinhala literacy, and English literacy. The results showed that all the variables except for Religion, are statistically significant. Married and widowed women are less likely to participate in the labor force compared to never-married women, while separated and divorced women are more likely to participate. Women in districts like Nuwara Eliya, Kilinochchi, Kurunegala, Anuradhapura, Badulla, and Rathnapura have higher labor force participation rates. There is a notable gender gap in labor force participation, with males participating more actively than females; over half of the working-age female population remains economically inactive. Females constitute most of the unemployed demographic. Despite being more prevalent in urban and rural areas, labor force participation rates are higher in the estate sector. Most women abstain from job searches due to household responsibilities and education levels. Nearly half of discouraged women are concentrated in younger age groups, with 26% aged 25-34 and 21% aged 35-54. The study underscores the necessity of policy interventions to address barriers to women's labor market participation, especially in household duties and education, to enhance Sri Lanka's female workforce potential and contribute to its economic and social development.
Analysis of the interrelationship between weather parameters in Colombo area
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Samarasinghe, D. G. S. P.; Hewaarachchi, A. P.; Dissanayaka, D. M. P. V.
Colombo serves as the administrative, legal, and primary urban hub of Sri Lanka in terms of population. Its metropolitan vicinity houses around 5.6 million residents, with 752,993 within the Municipality. The city of Colombo is renowned for its tropical climate, characterized by consistently high average temperatures, substantial annual rainfall, and other meteorological factors. This study attempted to investigate the variations in the weather parameters and to model the interdependencies among those variables. The correlations of five weather parameters between January 2007 and May 2022 were analysed based on the monthly data collected from the Department of Meteorology in Colombo area. Rainfall, Minimum Relative Humidity, Maximum Relative Humidity, Wind Speed, and Temperature were considered in this study. Investigation of the correlation among various weather parameters holds paramount importance in understanding the intricate dynamics of Colombo's climate. A seasonal pattern was found in Minimum Relative Humidity, and high fluctuations were observed in Wind Speed and Rainfall out of the five weather parameters under study. Further, the study concluded that there is a moderate positive correlation (r = 0.5) between Rainfall and Minimum Relative Humidity and as well as between Rainfall and Maximum Relative Humidity (r = 0.7). A moderate negative relationship (r = -0.5) between Wind Speed and Maximum Relative Humidity is also found in this case study. In this research, vector autoregressive (VAR) models were employed to capture the relationships among weather parameters which indicated the presence of Granger Causality. According to the Granger Causality test it was found that Minimum Relative Humidity can be used in predicting the other four parameters i.e., Rainfall, Maximum Relative Humidity, Wind Speed, and Temperature. Maximum Relative Humidity can be used in predicting the Minimum Relative Humidity, Wind Speed, and Temperature. Also, Wind Speed can be used in predicting all other four weather parameters concerned in this study. It has been found that monthly average Temperature has the potential to serve as a predictive factor for all three of the weather parameters except Wind Speed under consideration in this investigation.
Assessment of the State of Quality in garments applying Data mining mechanisms: A Case Study in the Apparel Industry
(Faculty of Commerce and Management Studies University of Kelaniya, Sri Lanka, 2020) Basnayake, B. R. P. M.; Hewaarachchi, A. P.; Chandrasekara, N. V
Forecasting the quality of sewed garments is an important area in the apparel industry. This paper consists of a case study relevant to a high-ranking apparel manufacturing plant in Sri Lanka. Quality is measured using the First Time Through (FTT) state which is a measure of production competence and capacity. The factory capacity is to afford the FTT 98% or above as a high state category. The low state is consisted of FTT of less than 98%. Recently Data mining methods are used to extract insights from data and to make fast decisions. The main objective of the study is to identify the better model to predict the FTT state with data mining mechanisms. Classification tree and Probabilistic Neural Network (PNN) models were used to forecast the FTT state with the under-sampling method due to the matter of class imbalance in the original dataset. True positive (TP), False-positive (FP), precision, recall, accuracy and F-measure were used as the performance measurements. FP rate was zero and precision was one in the classification tree. While the FP rate was 0.0649 and precision was 0.9348 in the PNN model. Both models had a high F-measure value of 0.9745 and 0.9287 respectively. Therefore, two models can be used in prediction with better performance measurements. Outcomes of the study will help to find out the optimum allocation of a style to a relevant team to achieve the highest FTT state, to recognize the training requirements of the employees and to improve the satisfaction of the customer.
Detecting abrupt changes in thermal electricity production data in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Sumathipala, P. L. N. S.; Hewaarachchi, A. P.; Dissanayaka, D. M. P. V.
A changepoint or an abrupt change is a distributional change in a time series data structure. Over the past years, many studies have been conducted to search these changepoints and many researchers proposed several multiple changepoint detection methods. One such search method is the Pruned Exact Linear Time (PELT) method, which is exact and under mild conditions, has a computational cost which is linear in the number of data points. This method is a more accurate and faster method to detect multiple changepoints. The objectives of this study are to detect abrupt changes in thermal electricity data in Sri Lanka and predict thermal electricity production accurately. Since undetected changepoints may cause incorrect modelling or prediction, the accurate analysis of electricity data is vital. In this study, electricity production (Hydro, Thermal oil and coal, and wind) by Ceylon Electricity Board (CEB) in Sri Lanka for the period 2000 to 2019 was used to find abrupt changes. The PELT method is used to detect these changepoints and their location in the variance of electricity data. First, the total electricity production of oil and thermal data were used and a changepoint was found in April 2011. This is a documented changepoint since, according to CEB Annual Report 2011, 1487 GWh of thermal (oil) power was added to the system during 2011, which was a significant change. Moreover, two models, for the periods 2000 to 2019 and 2011 to 2019 (after the detected changepoint) were fitted for forecasting the production. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were calculated to compare the forecasting accuracy of these models. The first model (ARIMA (2,1,3)), which does not consider the changepoint structure, results in RMSE and MAPE values of 0.911 and 6.009, respectively, for the period 2000 to 2018 for the thermal electricity data. For the second model (ARIMA (1,0,3)), RMSE and MAPE were 0.244 and 3.267, respectively, for the period 2011 to 2018. It can be seen that the models fitted by considering changepoints give more accurate results for forecasting electricity production.
Economic impact of COVID-19 on the total revenue of the textile and apparel export industry in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Kaushalya, H. A. T.; Priyadarashana, D. A. D. S.; Liyanage, U. P.; Hewaarachchi, A. P.; Jayamanna, J. M. A. D. A. P.; Virani, D. G. D.; Dilshan, H. R.; Viduranga, A. L.
Textile and apparel exports play a vital role in economic development in Sri Lanka. It’s approximately 44% of total exports in the country by 2024. The apparel sector has become complicated in recent times because of economic instability followed by COVID-19. The purpose of this study is to quantify the revenue impact caused by COVID-19 on the textile and apparel export industry in order to address this significant issue. This will facilitate plans by textile and apparel exporters more appropriately. The COVID-19 period has resulted a negative impact on the Sri Lankan textile and apparel industries. If the industry faces such an unfortunate epidemic in the future, it is beneficial to know the impact of COVID- 19 on the industry. Thus, the decision-makers and experts in the industry will be able to make sound decisions and plan effectively to reduce the impact. This study has considered monthly export revenues from January 2009 to April 2024. Data were collected from the Joint Apparel Association Forum Sri Lanka (JAAFSL). The revenue data before the COVID-19 period, from January 2009 to December 2019, was considered in the model-building process. First, the preliminary transformation, including the log transformation, first differencing, and seasonal differencing, were applied to obtain a stationary data set. Then, fifteen candidate models were selected based on the Akaike Information Criteria (AIC). ARIMA (0,1,1) (0,1,1)12 model has been selected, which has the lowest AIC value and MAPE value of 1.29%. Further, the model diagnosis was checked using a residual analysis. The p-value of the LjungBox Q-test is 0.148, which confirmed that the residuals are white noise. After that, the monthly revenue was predicted for the COVID-19 period using the fitted model. To quantify the impact of COVID-19 two curves were fitted to the actual revenue and the predicted revenue after COVID-19. The difference between areas under both curves was then computed. The impact has been estimated as a percentage of this difference to the predicted revenue. The results of this study arrived at the high impact of an 11.92% decrease in export revenue due to the COVID-19 pandemic on the textile and apparel export industry, with the estimated loss equivalent to 1899.1291 million USD. This figure underscores the significant impact on the export industry and the country's economy. Such understanding will assist experts and decision-makers in evolving their strategies to move out of such situations in the future, providing fresh mechanisms to avoid this kind of impact.
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.
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
A quantitative analysis of fishery industry in modelling of production, trade dynamics, and COVID-19 impact estimation
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Sandamini, R. P. K.; Tharaka, K. D. R.; Wickramasinghe, W. A. P. K. V.; Mendis, T. A. B.; Wijesekara, J. M. C. D.; Lakruwan, J. M. D. C.; Dasanayaka, O. R. G. K.; Liyanage, U. P.; Hewaarachchi, A. P.
Sri Lanka, being an island, is granted an immense maritime boundary. The fishery industry is a prominent and significant part of the Sri Lankan economy, contributing around 1.5% of the Gross Domestic Product. The objectives of this research are to identify the factors affecting fish production, analyse the Net Trade Balance (cost difference between import and export of fishery products) in the fishery industry, and estimate the impact of COVID-19 on fish production. The monthly data from 2015 to 2022 is taken from the Statistics unit of the Ministry of Fisheries, Sri Lanka. This data is analysed, and the findings revealed that fish production in the country had decreased significantly over time, and thereby, it has impacted the import quantities as well. Further, almost 80% of the total fish production of the country is contributed by marine fish production and 20% by inland fish production. The variations in fish production are largely caused by the inland fish harvest; that is marine fish production has monotone dynamics. Concerning the seasonal pattern, less production is visible during May and June due to the monsoon. However, a clear upward trend in the inland fish harvest is visible during this time due to the renewal of freshwater. According to a recent survey in the Indian Ocean, the decrement in the fish population and habitats contributed to the lower harvest of fish production. Further, it has been identified the factors of fuel prices, fishing gear costs, ice cube prices, and unauthorized fish catch by foreign fishermen, as the other affecting factors in fish production. Concerning fisheries export, Tuna fish and prawns play a vital role in the export market. Despite the marine resources, Sri Lanka still imports fisheries products by spending foreign remittances. The Net Trade Balance (NTB) of fisheries products in Sri Lanka is investigated in this research and modelled by multiple linear regression models (net trade balance as the response variable, and harvest of 10 fish types as independent variables, based on significance) for pre and post, COVID-19 pandemic conditions. Further, the models can accurately predict the NTB (Pre-COVID model R2 = 72.4%, post-COVID model R2 = 80.6%). This model can be used in policy and strategy analysis by respective authorities such as the Ministry of Fisheries, Sri Lanka. Using the time series methods (Moving Average, Exponential Smoothing, and SARIMA), fish production is analysed. Combining these models, the impact of the COVID-19 pandemic on fish production from February to August 2020 is estimated at 15.81%. In conclusion, this research identified the fish production patterns, COVID-19's impact on production, and a model to estimate NTB, which also can be an analytical tool for the policymaking of the fisheries industry.
A quantitative characterization of sector-wise performance interdependencies in stock market using changepoint and performance-induced distant clustering
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kavishka, R.T.; Liyanage, U. P.; Hewaarachchi, A. P.; Dissanayaka, D. M. P. V.
The Colombo Stock Exchange (CSE) is the Sri Lankan marketplace for companies to trade their stocks to the public. There are 19 sectors in the CSE before the Global Industry Classification Standard (GICS) classification. The stock market has become a significant icon in most of the country’s economy. Due to the enhancement of the data science discipline, quantitative research on the stock market has gained popularity among scholars in the recent past. Most of the studies were conducted to predict the value of a stock and its volatility. However, this study explored potential performance dependencies among the 19 industrial sectors registered at the CSE. In known literature, this scope has not been addressed quantitatively. The sectorial All Share Price Index (ASPI) is used to characterize the sector interdependencies, as the volatility of ASPI implies the sector performance at a given time or a short period. Because diverse sector movements can offset each other, leading to a stable index, while extreme sector-specific events or trends can result in increased index volatility. The ASPI indices published by CSE from 2005 to 2019 were considered in the analysis. The persistency of ASPI volatility in a compact interval indicates the consistency of the performance in each sector. Thus, the comparison of volatility changes and their changing time, i.e., changepoint analysis, describes the changes in the sectorial performances. Consequently, the interdependencies among the sector-wise performances can be recognized by the emerging patterns of the changepoints, i.e., the clustered behaviour of the changepoints. Through this approach, the investigation seeks to identify significant transitions or shifts in the behaviour of each sector. Non-parametric methods were employed in the identification of the changepoints of the ASPI series. The standard clustering approaches could not be utilized in grouping the changepoints as the clustering metrics defined by the variation of performances are interconnected. Thus, a new clustering approach was developed using a cluster performance-induced distant measure defined based on a reference industrial sector. This analysis resulted in interdependency among the industrial sectors. Further, variation patterns among the changepoints were identified using interval scaling, and dependent industrial sectors were identified with the help of the performance-induced distant clustering approach. For example, the Bank Finance & Insurance, Telecommunication, and Trading sectors exhibited strong interdependencies. Also, the Construction & Engineering, Oil palms, and Hotel & Travels sectors exhibited strong interdependencies. So far, in qualitative relations, these interdependencies were merely recognized by the gut feelings of financial analysts. Nevertheless, this study provides a clear quantitative characterization of performance dependencies, and thus, the findings are crucial for determining investment strategies and minimizing risk in stock exchanges.
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.
Time series forecasting of farm gate prices of fresh coconuts in three major coconut growing areas of Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2021) Perera, D. H. N.; Waidyarathne, K. P.; Jayasundara, D. D. M.; Hewaarachchi, A. P.
Coconut is a perennial crop with important food value and other endless uses for human beings. Hence, this has led to the emergence of a diversified set of industrial activities. All over the world, Sri Lanka is the fourth largest coconut producing country. The major part of Sri Lanka's coconut production comes from the Coconut Triangle, which consists of Puttalam, Kurunegala and Gampaha districts. Forecasting coconut prices can provide critical and useful information to coconut growers making production and facing real situations and uncertainties of the coconut industry. The objective of this study is to build accurate univariate or multivariate time series models to forecast the farm gate prices of fresh coconut in three major coconut growing areas (Puttalam, Kurunegala, and Gampaha) of Sri Lanka. This study evaluated the times series data on monthly farm gate prices of fresh coconut in the selected districts from January 2009 to December 2019.This paper examines three time series modelling approaches, Autoregressive Integrated Moving Average (ARIMA), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) and Vector Error Correction (VEC) model. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to evaluate the performance of fitted models. As the univariate time series approach, ARIMA (1,1,5) and ARIMA (2,1,2) were identified as the better models for forecasting prices of Puttalam and Gampaha based on Akaike Information Criterion (AIC) where RMSE (5.83,5.77) and MAPE (12.60,10.99) respectively. In contrast to the other two districts, Kurunegala showed a non-constant variance with the time, hence GARCH model approach was tested for the particular data series. It was found that all model coefficients were not significant in the GARCH model thus univariate models were not applicable for Kurunegala District. Therefore, multivariate time series model was carried out to find a suitable model. First, the Johansen co-integration test was applied and the results proved that there were two co-integration equations at 5% level of significance. As there were significant cointegration detected between series, VECM was applied in order to evaluate the short run properties of the cointegrated series. According to the lag selection criteria, lag 7 was selected as the optimum lag value. Considering the VEC models, the RMSE and MAPE in Puttalam, Kurunegala and Gampaha were 6.30,5.41,5.85 and 12.81,10.76,11.14 respectively. Results revealed that VECM approach worked well for forecasting Kurunegala price series. Even with long-term equilibrium relationship exists between series, VECM approach was less accurate in defining the relationship in comparison to ARIMA models for Puttalam and Gampaha price series. Therefore, that the study recommends the ARIMA models as the appropriate models to forecast the monthly farm gate prices of fresh coconut in Gampaha and Puttalam districts.