References

Abeynayake, W.H.E.B.P. and Weerapura, N.R. 2013. Forecasting of Tea Production Using Time Series Models. Proceedings of 12th Agricultural Research Symposium: 351–355.
Annamalai, N., & Johnson, A. (2023). Analysis and forecasting of area under cultivation of rice in India: Univariate time series approach. SN Computer Science, 4(2), Article 193. https://doi.org/10.1007/s42979-022-01604-0
Borah, P., & Amrin, S. (2022). Future Projection of Production of Tea In Assam : By Using ARIMA Model. IJFANS International Journal of Food and Nutritional Sciences, 11(3), 1474–1482.
Box, G. E. P. (2013). Time Series Analysis, Forecasting and Control. John Wiley & Sons
Cavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics, 11(3). https://doi.org/10.1002/wics.1460
Central Bank of Sri Lanka. (1991). Annual report 1990
Central Bank of Sri Lanka. (2020). Economic and Social Statistics of Sri Lanka 2020 (Vol.42).
Central Bank of Sri Lanka. (2021). Annual report 2020. https://www.cbsl.gov.lk/en/publications/economic-and-financial-reports/annual-reports/annual-report-2020
Central Bank of Sri Lanka. (2023). Annual report 2022: National output, expenditure, income and employment. https://www.cbsl.gov.lk/sites/default/files/cbslweb_documents/publications/annual_report/2022/en/6_Chapter_02.pdf.
Deka, S., Hazarika, P. J., & Patowary, A. N. (2022). Tea production in Assam: Forecasting with ARIMA model. Annual Scientific Report Dibrugarh University, 33(1), 48–56.
Dharmadasa, M., Zubair, L., Nijamdeen, A., & Najimuddin. N.  (2018). Review of Tea Industry in Sri Lanka for Climate Analysis. Dilmah Conservation.  https://www.dilmahconservation.org/pdf/review-of-tea-industry-in-sl.pdf.
Esham, M., & Garforth, C. (2013). Climate change and agricultural adaptation in Sri Lanka: A review. Climate and Development, 5(1), 66–76. https://doi.org/10.1080/17565529.2012.762333
Export Development Board. (2022). Industry capability report: Tea sector (Camellia sinensis). Sri Lanka Export Development Board. https://www.srilankabusiness.com/ebooks/industry-capability-report-tea-2021.pdf
Fernando, P. W.S., Nianthi, R.  & Subasinghe, S. (2023). Evaluating Tea Land Suitability and Potential Challenges in the High Grown Region (HGR) of Sri Lanka: A Case Study of Kothmale DS Division, Sri Lanka. IOP Conference Series: Earth and Environmental Science 1266(1). https://doi.org/10.1088/1755-1315/1266/1/012016
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008
Hilal, M., & Ismail, M. (2019). Sri Lanka’s tea economy: Issues and strategies. Journal of Politics and Law, 13(1), 1–10. https://doi.org/10.5539/jpl.v13n1p1
Hossain, M. M., & Abdulla, F. (2015). Forecasting the tea production of Bangladesh: Application of ARIMA model. Jordan Journal of Mathematics and Statistics, 8(3), 257–270.
Hyndman, R. J. (2002a). ARIMA Processes. In H. Daellenbach & R. Flood (Eds.), The informed student guide to management science (pp. 27–28). Cengage Learning Business Press.
Hyndman, R. J. (2002b). Computer-assisted time series analysis (3rd ed.). Monash University, Department of Econometrics and Business Statistics. https://robjhyndman.com/papers/catsa.pdf
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts. https://otexts.com/fpp3/
International Labour Organization. (2018). Future of Work for Tea Smallholders in Sri Lanka. ILO Country Office for Sri Lanka and the Maldives.
Islam, M. A., Sumy, M. S. A., Uddin, M. A., & Hossain, M. S. (2020). Fitting ARIMA model and forecasting for the tea production, and internal consumption of tea (per year) and export of tea. International Journal of Material and Mathematical Sciences, 2(1), 8–15.
Kim, J., Kim, H., Kim, H., Lee, D., & Yoon, S. (2024). A comprehensive survey of time series forecasting: Architectural diversity and open challenges. arXiv. https://doi.org/10.48550/arXiv.2411.05793
Kumarasinghe, H.P.A.S.S., & Peiris. B.L.  (2018). Forecasting annual tea production in Sri Lanka. Tropical Agricultural Research, 29(2), 184–93.
Mahanta, K.K. (2023). A time series analysis of tea production in South Bank of Assam from 1961 to 2013 Using ARIMA Model. International Journal of Agricultural and Statistical Sciences 19(02), 725–32.
Mech, A. (2017). Status of tea production in Assam: Past trends and its future projections. Journal of Open Learning and Research Communication (JOLRC), 3, 45–56.
Mertens, W., Pugliese, A., & Recker, J. (2017). Quantitative data analysis: A companion for accounting and information systems research. Springer International Publishing. https://doi.org/10.1007/978-3-319-42700-3
Mila, F. A., Noorunnahar, M., Nahar, A., Acharjee, D. C., Parvin, M. T., & Culas, R. J. (2022). Modelling and Forecasting of Tea Production, Consumption and Export in Bangladesh. Current Applied Science and Technology 22(2), 1–20.
Mohotti, K. M. & Shaymalie, H W. (2024). Economic and Social Evolutions of the Tea Industry in Sri Lanka : Lessons for Sustainability in Responsible Business and Way Forward. Sri Lanka Economic Journal, 21(Special Issue), 154–170.
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). John Wiley & Sons.
Niranjan, H. K., Kumari, B., Raghav, Y. S., Mishra, P., Al Khatib, A. M. G., Abotaleb, M., & Supriya. (2022). Modeling and forecasting of tea production in India. Journal of Animal and Plant Sciences, 32(6), 1598–1604. https://doi.org/10.36899/JAPS.2022.6.0569
Prabhakaran, S. (2021). ARIMA Model - Complete guide to time series forecasting in Python. Time Series. 1–43. https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/.
ProjectPro. (2023). Solved big data and data science projects. https://www.projectpro.io
Rink, K. (2021, October 21). Time series forecast error metrics you should know. Medium. https://medium.com/data-science/time-series-forecast-error-metrics-you-should-know-cc88b8c67f27 (researchgate.net preview link)
Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With R examples (4th ed.). UCLA Statistics & Data Science. http://www.stat.ucla.edu/~frederic/tsa4
Sri Lanka Tea Board. (2014). Annual report 2014. https://srilankateaboard.lk/uploads/2021/04/Annual-Report-2014.pdf
Wei, W. W. S. (1990). Time series analysis: Univariate and multivariate methods. Addison Wesley. https://doi.org/10.2307/2289741
Wijeratne, M. A., Anandacoomaraswamy, A., Amarathunga, M. K. S. L. D., Ratnasiri, J., Basnayake, B. R. S. B., & Kalra, N. (2007). Assessment of Impact of Climate Change on Productivity of Tea (Camellia Sinensis L.) Plantations in Sri Lanka. Journal of the National Science Foundation of Sri Lanka, 35(2), 119–26.
Wilson, G. T. (2016). Time series analysis: Forecasting and control (5th ed.) by G. E. P. Box, G. M. Jenkins, G. C. Reinsel & G. M. Ljung. Journal of Time Series Analysis, 37(5), 709–711. https://doi.org/10.1111/jtsa.12194
Yaffee, R. A., & McGee, M. (1999). An introduction to time series analysis and forecasting: With applications of SAS and SPSS. Academic Press.