Browsing by Author "Asanka, P. P. G. D"

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A case study of Sri Lanka oil price fluctuations and its influencing factors using predictive analytics
(IEEE, 2016-12-19) Kandawala, D. S. A; Ramanayake, R. T; Bogahawatte, K. G. L; Mansoor, M. A. M; Wanniarachchi, D. M; Asanka, P. P. G. D
Oil is one of the most crucial commodity and energy resource that guarantee the evolution of the economy and industry of a country. The price fluctuation of the oil would be the emerging factor to be concerned and discussed generally in political and economic circle in each and every country. In Sri Lanka also the problem would be same when it comes to oil industry and its influencing factors for price fluctuation. This paper provides a comprehensive implementation of data warehousing process for the petroleum industry data set. Based on the data collection, a broad data analysis has been conducted to discover patterns of oil prices and sales variation with respect to the political and economic impact factors of Sri Lanka. Through the analysis, it has proven that the influencing factors would affect to the oil prices and sales accordingly. Furthermore, this aims to present few predictions based on the analysis.
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Impact Analysis of US Dollar Index Volatility on Imports and Import Categories of Sri Lanka
(IEEE, 2018-07-31) Sahabandu, R. V; Asanka, P. P. G. D
The economic liberation in 1977 resulted in drastic changes in many aspects of Sri Lanka. Considering about 1978-2015, the country yearly import demand represents over 30% share of the gross domestic product (GDP) except 1984, 2009, 2010, 2013-2015. Investigations and the studies on a countries' imports are surprisingly overlooked as there are several studies being carried out focusing only the aggregated export volume concerning the exchange rate volatility. The monthly data of Sri Lanka imports, import categories and monthly US Dollar (USD) volatility from January 2007-December 2016 were used for the analysis. This study tries to learn the impact of US Dollar Index (USDX) volatility on import demand of Sri Lanka. The Autoregressive Distributed Lag (ARDL) Approach is employed to learn long-term and short-term cointegration among the underlying variables. There exists a 95% statistically significant short-run relationship and it is identified that the import categories, Consumer Goods (CG), Intermediate Goods (IG), Investment Goods (INV), Unclassified Items (UI), None-Oil Imports (NO) have a speed of adjustment to the equilibrium (SAE) in the long-run of 17%, 36%, 23%, 23%, 25% respectively. The total imports reveal that the disequilibrium conditions will be resolved by 27% within a period of one month that is shocked due to the USDX volatility. Knowledge of the relationship between USDX fluctuation, exchange rate volatility and import volume will support to pursuit for a beneficial trade and prevent or be prepared for a much more stable situation within Sri Lanka.
Open Access
Recommending a Model to Forecast Sri Lanka Wholesale Price Index Using Big Data Analytics
(IEEE, 2018-02-22) Thakshila, P. M. C; Asanka, P. P. G. D
The Whole Sale Price Index (WPI) is a main index, which is used to measure price variance before a product or service release to a consumer. WPI represents the basket of wholesale goods and services on market basket. Sri Lanka WPI is accumulated using Laspeyre's formula considering based year as 1974 and up till now not seasonally adjusted. Data collection, compilation, and Dissemination of WPI are done by Prices, Wedges, and Employment division of the Statistics Department of Central bank of Sri Lanka (CBSL) and releasing to public every month. Forecasting of WPI is necessary to understand the aid primary level economic impact of the country. Big data analysis and Data mining are using for data where it is hard to handle using traditional tools and techniques. Decision makers able to gain valuable insights analyzing that varied and rapidly changing data. Time series analysis compromise method for analyzing time series data in order to extract meaningful statistics and other characteristics of data. This review discusses the way to utilize big data analysis technology to systematically analyze time series based WPI data in Sri Lanka. The time series based forecast technologies ARIMA, ANN, VAR, Moving Average, AFARIMA etc. are reviewed based on previous findings. Based on the result will present the effective model to forecast WPIs in Sri Lanka and will critically evaluate selected WPIs. That selection will coordinate based on the weight and relationship to all items based WPI. WPI will compare with existing Sri Lankan Price Indices based on the relational factors.