Browsing by Author "Basnayake, B. R. P. M."

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An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques
(The Journal of Agricultural Sciences - Sri Lanka, 2022) Basnayake, B. R. P. M.; Kaushalya, K. D.; Wickaramarathne, R. H. M.; Kushan, M. A. K.; Chandrasekara, N. V. C.
Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities.
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.
Performance of seasonal and double seasonal autoregressive integrated moving average models with ARCH/GARCH in forecasting exchange rates in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Basnayake, B. R. P. M.; Chandrasekara, N. V.
The exchange rate is one of the most essential economic indices and forecasting its chaotic and uncertain behaviour is challenging for business practitioners and academic researchers. This study mainly evaluated the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Double SARIMA (DSARIMA) with Autoregressive Conditional Heteroskedasticity (ARCH)/ Generalized ARCH (GARCH) models in forecasting daily exchange rates in Sri Lanka. This is the first study that used DSARIMA models with ARCH/GARCH of different specifications of error distributions, as previous studies focused on either on annual or weekly seasonality separately in forecasting exchange rates. The study considered USD, EURO, JPY, GBP, AUD, CAD, SGD and CHF against LKR, daily exchange rates from 1st January 2008 to 28th February 2022. Data were split non-randomly for training from 1st January 2008 to 07th January 2022 and the remainder for testing. The stationary of the exchange rates was checked, and the weekly and annual seasonality patterns were examined from the tests of Webel-Ollech (WO), Friedman rank (FR), and Kruskal-Wallis (KW). Model diagnostics checking was carried out with the tests of Ljung-Box, Jarque–Bera, and ARCH to check the presence of autocorrelation, normality, and heteroskedasticity in the residuals, respectively. The ARCH/GARCH specifications of normal, skew-normal, student-t, and skew-t were applied, as the correct innovation of the appropriate error distribution increases the accuracy of the fitted volatile model. Moreover, DSARIMA models were compared with the Seasonal Autoregressive Integrated Moving Average (SARIMA) models considering several performance criteria which were calculated from the original test values and forecasted values. Transformations of log and differencing were applied respectively to convert all the non-stationary exchange rates to stationary. Overall, weekly and annual seasonality patterns were observed for all the exchange rates from the results of WO, FR, and KW tests, except for FR test results, indicated that there is no annual seasonality in every exchange rate. Hence, SARIMA and DSARIMA models were fitted incorporating weekly and annual seasonality separately and together, respectively. Here, the seasonality feature was included using Fourier terms as external regressors to the ARIMA process. In conclusion, the compared results between fitted models favoured SARIMA for CHF against LKR, SARIMA with ARCH/GARCH for USD, EURO, JPY, GBP, and AUD against LKR, and DSARIMA with ARCH/GARCH models for CAD and SGD against LKR with the lower values. Overall, predicted values captured the behaviour of the exchange rates. However, a considerable number of volatile movements of the currency exchange rates were not very well captured, and they were observed by the graphs of actual vs fitted. Hence, as future work, this study proposes to build a time-series extension model incorporating the real distribution of the exchange rates. Nevertheless, the knowledge from the results of this study is important in managerial and financial decision makings and many others. Further, this study will add more value to the existing literature.