SLIIT International Conference on Advancements in Sciences and Humanities [SICASH] 2023

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Author: search.filters.author.Peiris, T. S. G.Has files: Yes

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    PublicationOpen Access
    Forecasting Consumer Price Index in the United States
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Witharana, W. W. S. K; Udugama, U. K. D. T. N.; Fernando, P. M. R.,; Kaumadi, H. M. H.; Peiris, T. S. G.
    This report presents the Auto-Regressive Integrated Moving Average (ARIMA) model for forecasting the consumer price index (CPI) in US using monthly data from March 2010 to March 2023. The original series was not stationary, but the first difference series was found to be stationary using the Augmented Dicky Fuller test. The best-fitted model was identified based on the significance of the parameters, volatility (sigma2), log-likelihood, Akaike, Schwartz, and Hannan- Quinn information criterion. Parameters of the fitted model are significantly deviated from zero. The stability of the model has been checked using the roots of the unit root test. Residuals of the fitted model satisfied the randomness but nonconstant variance. The monthly forecasted values of CPI from April 2023 to August 2023 are 301.833, 302.444, 303.038, 303.639, and 304.261. The percentage errors of the forecasted values are less than one percent. This method and results provide useful information to policy and market makers for their planning,
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    PublicationOpen Access
    Modeling Annual Coffee Production in Sri Lanka
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Jithmini, D.; Paboda, K. R.; Samaraweera, M. P. I. N.,; Welaramba, W. D. K. H,; Peiris, T. S. G.
    Coffee production is a source of revenue for the economic sector in Sri Lanka. During 1988 to 2020, the mean annual coffee production is 7672 metric tons with a coefficient of variation is 30%. The advanced knowledge of annual coffee production has many advantages. However, past studies found that no model has been developed to model annual coffee production in Sri Lanka. In this study, an ARIMA (1,2,0) model was identified as the best fitted model to forecast the annual coffee production. The model was trained using data from 1988 to 2020 and validated using data in 2021. The best-fitted model was selected by comparing different statistical indicators such as Akaike Information Criteria, Schwarz Criteria, Log-likelihood Criteria, and volatility of the three parsimonious models. It was found that the errors of the best fitted model were white noise. The percentage errors for the forecast values for the training and validation data sets were within ± 10. The predicted annual production for 2022, 2023, 2024, and 2025 are 6987 MT, 6221 MT, 7209 MT, and 6664 MT, respectively. This is the first empirical study to develop a statistical model to predict annual coffee production in Sri Lanka. The model can be improved by using external variables as explanatory variables and considering dummy variables to capture the structural breaks.