SLIIT International Conference on Advancements in Science and Humanities [SICASH]

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SLIIT International Conference on Advancements in Science and Humanities is organized by the Faculty of Humanities and Sciences of the Sri Lanka Institute of Information Technology (SLIIT), the annual research multi-conference of the faculty.

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    PublicationOpen Access
    Predictive Model for Monthly Made Tea Production in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Subasinghe, C; Wattegedara, N; Silva, T; Balasooriya, S; Dassanayake, K; Guruge, M.L
    This study forecasts monthly tea production in Sri Lanka by developing a suitable time series model to identify future trends in the national tea industry. The analysis is based on monthly made tea production data from January 2000 to June 2025, obtained from the Central Bank of Sri Lanka and the Sri Lanka Tea Board. After confirming the non-stationarity of the original series through the Augmented Dickey-Fuller test, both first-order and seasonal differencing were applied to achieve stationarity. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plotswere used to identify potential model structures.
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    PublicationOpen Access
    Development of SARIMA Model to Predict Quarterly Apparel and Textile Export Revenue in Sri Lanka
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Piyasiri, K. G. V.; Kasthuriarachchi, U. P; Nirmani, K. G. R; Tilakaratne, K.I.; Peiris, T. S. G
    Apparel and textile exports play a significant role in the Sri Lankan economy. The USA, UK, Italy, Germany, and Belgium are the main markets of apparel and textile exports in Sri Lanka. Advanced knowledge of export revenue is vital important for various reasons. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model of the type (1,1,0) x (0,1,1)4 was developed to model apparel and textile export revenue in Sri Lanka using quarterly data from year 2004 quarter 1 (2004Q1) to year 2021 quarter 4 (2021Q4). The errors of the model were found to be random and have a constant variance. The best fitted model was identified by comparing various statistical indicators, namely, the Akaike info criterion, Schwarz criterion, Hannan-Quinn criterion, Log likelihood criterion and volatility of six possible models decided based on sample ACF and PACF of the stationary series. The model was validated for data from year 2022Q1 to 2023Q1. The Mean Absolute Percentage Error (MAPE) for the training data set and validation data set were 7.68% and 11.35% respectively. The predicted revenues (Mn USD) for the 2023Q2 to 2024Q4 are 1074.23, 1263.30, 1222.22, 1206.74, 1058.38, 1265.00 and 1216.58, respectively. The forecasted values for short-term periods can be effectively used by the decision makers for various activities. The model developed is easy to use and reliable.