IJMSC Vol. 9, No. 2, 8 May 2023
Cover page and Table of Contents: PDF (size: 537KB)
Full Text (PDF, 537KB), PP.31-38
Views: 0 Downloads: 0
Jute Export, Exponential Smoothing Method, Holt’s Method, Smoothing Constants
Forecasting is estimating the magnitude of uncertain future events and provides different results with different supposition. In order to identify the core data pattern of jute bale requirements for yarn production, we examined 10 years' worth of data from Jute Yarn/Twin that were shipped by their member mills Limited. Exponential smoothing and Holt’s methods are commonly used to forecast this output because it provides an adequate result. Selecting the right smoothing constant value is essential for reducing predicting errors. In this work, we created a method for choosing the smoothing constant's ideal value to reduce study errors measured by the mean square error (MSE), mean absolute deviation (MAD), and mean square percent error (MAPE). At the contrary, we discuss research finding result and future possibility so that Jute Mills Limited and similar companies may execute forecasting smoothly and develop the expertise level of the procurement system to stay competitive in the worldwide market.
Md N. Dhali, Anirban Biswas, Al-Amin, Md M. Hasan, Nandita Barman, Md K. Ali, "The Forecast of Jute Export in Bangladesh for Optimal Smoothing Constants", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.9, No.2, pp. 31-38, 2023. DOI: 10.5815/ijmsc.2023.02.04
[1]Dhali, M. N., Rana, M. B., Islam, N., Roy, D., and Banu, M. S." Determination of Optimal Smoothing Constants for Foreign Remittances in Bangladesh ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.7, No.4, pp. 21-26, 2021. DOI: 10.5815/ijmsc.2021.04.02
[2]Barman, N., Hasan, M. B., & Dhali, M. N., 2018. Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh. The Dhaka University Journal of Science, 66(1), 55-58.
[3]Taylor, J. W., 2004. Smooth Transition Exponential smoothing. International Journal of Forecasting, 23, 385-394.
[4]Dhali, M. N., Barman, N., & Hasan, M. B., 2019. Determination of Optimal Smoothing Constants for Holt-Winter’s Multiplicative Method. The Dhaka University Journal of Science, 67(2), 99-104.
[5]Hasan, M. B., and M. N. Dhali, 2017. Determination of Optimal Smoothing Constant for Exponential Smoothing method & Holt’s method. Dhaka Univ. J. Sci. 65(1), 55-59.
[6]Paul, S.K., 2011. Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation. Global Journal of Research in Engineering, 11, Issue 3, Version 1.0.
[7]Ravinder, H. V., 2013. Determining the Optimal Values of Exponential Smoothing Constants – Does Solver Really Work? American Journal of Business Education, 6, May/June.
[8]Lim, P. Y., and C. V. Nayar, 2012. Solar Irradiance and Load Demand Forecasting based on Single Exponential Smoothing Method. International Journal of Engineering and Technology, 4, 4.
[9]Ravinder, H. V., 2013. Forecasting With Exponential Smoothing – What’s The Right Smoothing Constant? Review of Business Information System –Third Quarter, 17, No.3
[10]Singh, V. P., and V. Vijay, 2015. Impact of trend and seasonality on 5-MW PV plant generation forecasting using Single Exponential smoothing method. International Journal of Computer Applications, 130, 0975-8887.
[11]Bermudez, J. D., J. V. Segura, and E. Velcher, 2006. Improving Demand Forecasting Accuracy Using Nonlinear Programming Software. Journal of the Operational Research Society, 57, 94-100.
[12]Gardner, E. S., 1985. Exponential Smoothing: The State of the Art, part-I. Journal of Forecasting, 4, 1-28.
[13]Barman, N., Dhali, M. N., Hasan, M. B., 2021. Finding Appropriate Smoothing Constant for Demand Forecasting of Private Cars in Dhaka City, International Journal of Mathematics and Computation, 32(1).
[14]Gardner, E. S., 2006. Exponential Smoothing: The State of the Art, Part-II. International Journal of Forecasting, 22, 637-666.
[15]Hyndman, R. J., A. B. Koehler, R. D. Snyder, and S. Grose, 2002. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18, 439-454.
[16]Taylor, J. W., 2003. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19, 715-725.