การพยากรณ์ราคายางแผ่นรมควันชั้น 3 (RSS3) ด้วยตัวแบบห่วงโซ่มาร์คอฟ
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ม.ค. 29, 2019
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การพยากรณ์ ยางแผ่นรมควันชั้น 3 ตัวแบบห่วงโซ่มาร์คอฟ
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บทคัดย่อ
งานวิจัยนี้ศึกษาวิธีพยากรณ์ราคายางแผ่นรมควันชั้น 3 ด้วยตัวแบบห่วงโซ่มาร์คอฟ โดยใช้ข้อมูลรายวันของประเทศไทยตั้งแต่เดือนมกราคม 2557 ถึงเดือนมีนาคม 2559 จำนวนขนาดตัวอย่างทั้งหมด 536 ชุด งานวิจัยได้พิจารณาผลกระทบของขนาดตัวอย่างต่อความแม่นยำของตัวแบบ (536 ชุด และ 196 ชุด) และทดสอบผลในแต่ละอันตรภาคชั้น (ช่วงของราคา) ที่ระดับ 5, 10, 15, 20, 25, 30 อันตรภาคชั้น ผู้วิจัยสร้างทรานเชียนท์เมทริกซ์ และสุ่มช่วงของราคาเพื่อจำลองสถานการณ์จำนวน 1,000 ค่า พบว่า ขนาดตัวอย่าง 536 ชุด ที่ระดับ 15 อันตรภาคชั้น และขนาดตัวอย่าง 196 ชุด ที่ระดับ 5 อันตรภาคชั้นมีค่าเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์เฉลี่ยที่ใกล้เคียงกัน คือ 4 เปอร์เซ็นต์
Article Details
บท
บทความวิจัย ด้านวิศวกรรมศาสตร์
บทความที่ลงตีพิมพ์เป็นข้อคิดเห็นของผู้เขียนเท่านั้น
ผู้เขียนจะต้องเป็นผู้รับผิดชอบต่อผลทางกฎหมายใดๆ ที่อาจเกิดขึ้นจากบทความนั้น
References
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[2] Rubber Technology Research Centre “Research and development of rubber and Para-wood products under the cooperative network of manufacturers and research units,” Rubber Technology Research Centre, Faculty of Science, Mahidol University, Bangkok, Thailand, 2013.
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[16] J. Chuinakhorn and M. Rattanasiriwoo, “The system of rubber price forecast using neural technique. A case study: Rubber Research Institute of Thailand,” M.S. thesis, Department of Information Technology, Faculty of Information Technology, King Mongkut's University of Technology North Bangkok, 2008 (in Thai).
[17] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. Martinez-Muñoz, “Multilayer neural network with multi-valued neurons in time series forecasting of oil production,” Neurocomputing, vol. 175, Part B, pp. 980–989, 2016.
[18] A. Carpinone, M. Giorgio, R. Langella, and A. Testa, “Markov chain modeling for very-shortterm wind power forecasting,” Electric Power Systems Research, vol. 122, pp. 152–158, 2015.
[19] K. C. CHAN, “Market share modelling and forecasting using markov chain and alternative models,” International Journal of Innovative Computing, vol. 11, no. 4, pp. 1205–1218, 2015.
[20] S. Romprasert, “Forecasting of natural rubber ribbed smoked sheets no.3 (RSS3) price in the agricultural futures exchange of Thailand,” Ph.D. dissertation, Department of School of Development Economics, National Institute of Development Administration, 2010.
[2] Rubber Technology Research Centre “Research and development of rubber and Para-wood products under the cooperative network of manufacturers and research units,” Rubber Technology Research Centre, Faculty of Science, Mahidol University, Bangkok, Thailand, 2013.
[3] The Office of Industrial Economic, “Introduction to industrial rubber and rubber products,” Office of Industrial Economics, Bangkok, Thailand, 2014.
[4] Y. Kanasawat, “Thailand board of investment,” Investment Promotion Journal, vol. 23, no. 2, pp. 22–28, 2010.
[5] P. Putzon, “The forecasting of export quantity of the rubber ribbed smoked sheet 1 by Box-Jenkins method,” B. Sc., Department of Mathematics, Faculty of Science, Burapa University, 2013.
[6] P. Tapparak and P. Payakkapong, “Forecasting of agricultural product price in the futures market: A case study of rubber,” in Proceedings of 42nd Kasetsart University Annual Conference, 2003, pp. 125–131.
[7] S. Romprasert “Market efficiency test on RSS3 in agricultural futures exchange,” Applied Economics Journal, vol. 15, no. 2, pp. 55–78, 2008.
[8] S. Thongnok, “Forecasting short run price of smoked rubber no.3 in agricultural futures exchange of Thailand,” M.S. thesis, Department of Graduate School, Chiang Mai University, 2010.
[9] C. Sukpan, C. Chopaiad, and S. Chooduang, “Price forecasting of ribbed smoked rubber sheet no.3 in the agricultural futures market by leading indicators and artificial neural networks,” Thaksin University Journal, vol. 18, no.1, pp. 32–40, 2015.
[10] J. Sinthichok, “The unsmoke sheet rubber price forecasting model through support vector machine,”M.S. thesis, Department of Information Technology Management, Faculty of Engineer, Prince of Songkla University, 2012.
[11] C. Rattanavonsee, “Forecasting the price of rubber in the agricultural futures exchange of thailand,” Veridin E-Journal, Silpakorn University, vol. 6, no. 1, pp. 840–846, 2013.
[12] V.G. Kulkarni, Introduction to Modeling and Analysis of Stochastic Systems, 1se ed. Springer-Verlag New York, 2011.
[13] R. Durrett, Essentials of Stochastic Process. Springer, Cham, 2011.
[14] Y. Zhang, “Prediction of financial time series with hidden markov models” M.S. thesis, Department of School of Computing Science, Simon Fraser University, Canada, 2004.
[15] J. Eckstein, (2002, Dec), YASAI: Yet Another Add-in for Teaching Elementary Monte Carlo Simulation in Excel [Online]. Available: http://pubsonline.informs.org/doi/abs/10.1287/ited.2.2.12.
[16] J. Chuinakhorn and M. Rattanasiriwoo, “The system of rubber price forecast using neural technique. A case study: Rubber Research Institute of Thailand,” M.S. thesis, Department of Information Technology, Faculty of Information Technology, King Mongkut's University of Technology North Bangkok, 2008 (in Thai).
[17] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. Martinez-Muñoz, “Multilayer neural network with multi-valued neurons in time series forecasting of oil production,” Neurocomputing, vol. 175, Part B, pp. 980–989, 2016.
[18] A. Carpinone, M. Giorgio, R. Langella, and A. Testa, “Markov chain modeling for very-shortterm wind power forecasting,” Electric Power Systems Research, vol. 122, pp. 152–158, 2015.
[19] K. C. CHAN, “Market share modelling and forecasting using markov chain and alternative models,” International Journal of Innovative Computing, vol. 11, no. 4, pp. 1205–1218, 2015.
[20] S. Romprasert, “Forecasting of natural rubber ribbed smoked sheets no.3 (RSS3) price in the agricultural futures exchange of Thailand,” Ph.D. dissertation, Department of School of Development Economics, National Institute of Development Administration, 2010.