Title

預測泰國鳳梨罐頭出口量

Translated Titles

Forecasting export quantity of canned pineapple in Thailand

Authors

張玉文

Key Words

ARIMA模型 ; SARIMA模型 ; SARMA(1,1,1)(1,1,1)12模型 ; SARMA(2,1,1) (1,1,1)12模型 ; 鳳梨罐頭 ; 預測 ; ARIMAmodel ; SARIMAmodel ; SARMA(1,1,1)(1,1,1)12model ; SARMA (2,1,1)(1,1,1)12model ; Canned pineapple ; Forecasting

PublicationName

中興大學應用經濟學系所學位論文

Volume or Term/Year and Month of Publication

2017年

Academic Degree Category

碩士

Advisor

黃琮琪

Content Language

英文

Chinese Abstract

本研究是利用sarima 和 arima box -jenkins模型來預測泰國的鳳梨罐頭出口量。希望透過本研究可以達到三個目的:(1)分析了解泰國對美國輸出鳳梨罐頭的數量 (2)分析了解泰國對歐盟輸出鳳梨罐頭的數量 (3)針對預測出的結果提出泰國出口政策的建議。本研究收集2012年1月至2016年12月間的180筆資料 (observation),並將數據量化分析,數據分析步驟依序為(1) 穩定檢定、 (2) 模型判別、 (3) 估計、 (4) 預測。藉由檢定從泰國到美國的鳳梨罐頭出口量之平穩性可以得知穩定值屬於第一個差異 (d=1) ;然而,檢定泰國對歐盟的鳳梨罐頭出口量之平穩性可以得知穩定值屬於平穩(d=0)。 若將數據以時間序列為穩定的條件下來擬合模型,可以得知泰國對美國的鳳梨罐頭 出口量之最符合模型為 SARIMA (2,1,1)(1,1,1)12;而泰國對歐盟的鳳梨罐頭出口量之最符合的模型則屬於SARMA (1,1,1)(1,1,2)12。本研究盼在評估出最貼切的模型之後,並以此模型預測下一年泰國對美國和歐盟的鳳梨出口量。

English Abstract

This paper studies the forecasting export quantity of canned pineapple in Thailand by using SARIMA and ARIMA Box-Jenkins models as forecasting methodology. Purposes of this study are, (1) to study canned pineapple export quantity from Thailand to the United States, (2) to study canned pineapple export quantity from Thailand to the European Union, (3) to interpret the estimated result for Thailand's export policy suggestion. This study uses quantitative analysis on secondary data, which has been collected from January 2002 to December 2016, total 180 observations. The study has analyzed in four parts including; 1) stationary checking; 2) model identifying; 3) estimating; 4) forecasting. After testing the stationarity of canned pineapple export quantity from Thailand to the United States, the data is stationarity at 1st difference (d=1). Moreover, the stationarity of canned pineapple export quantity from Thailand to the European Union is at the level (d=0). When time series are stationary, the study estimates the possible models. Most appropriate Models for canned pineapple export quantity from Thailand to the United States are SARIMA (2,1,1) (1,1,1)12 and SARIMA (1,1,1) (1,1,2)12 models for canned pineapple export quantity from Thailand to European Union. After estimating the most appropriate models, then the models are used to forecast canned pineapple export quantity from Thailand to the United States and from Thailand to European Union in one year forward.

Topic Category 農業暨自然資源學院 > 應用經濟學系所
社會科學 > 經濟學
Reference
  1. Amin, M., Amanullah, M., & Akbar, A. (2014). Time Series Modeling for forecasting wheat production of Pakistan. Plant Sciences, 24(5), 1444-1451.
    連結:
  2. Bogahawatte, V. S. a. C. (2012). Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model. Tropical Agricultural Research Vol. 24 (1): 21 - 30 (2012), pp. 1-30.
    連結:
  3. Chaiboonsri, P. B. S. K. C. and P. Chaitip (2009). "Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand."
    連結:
  4. Cholette, P. A. (1982). Prior information and ARIMA forecasting. Journal of Forecasting, 1(4), 375-383.
    連結:
  5. Cho, V. (2003). "A comparison of three different approaches to tourist arrival forecasting." Tourism management 24(3): 323-330.
    連結:
  6. Chu, F.-L. (1998). "Forecasting tourism demand in Asian-Pacific countries." Annals of Tourism Research 25(3): 597-615.
    連結:
  7. Eapsirimetee, P., Suthikarnnarunai, N., Member, IAENG, & Hanhirun, S. (2011). Canned Pineapple’s Demand Forecast Using Econometrics Model. Proceedings of the World Congress on Engineering and Computer Science, 1-4.
    連結:
  8. Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708.
    連結:
  9. Ho, S. and M. Xie (1998). "The use of ARIMA models for reliability forecasting and analysis." Computers & industrial engineering 35(1-2): 213-216.
    連結:
  10. Ho, S., et al. (2002). "A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction." Computers & industrial engineering 42(2): 371-375.
    連結:
  11. Hyndman, R. J., et al. (2002). "A state space framework for automatic forecasting using exponential smoothing methods." International Journal of Forecasting 18(3): 439-454.
    連結:
  12. Joo, Y. J., & Jun, D. B. (1997). State space trend‐cycle decomposition of the ARIMA (1, 1, 1) process. Journal of Forecasting, 16(6), 411-424.
    連結:
  13. Khalid Khan, G. K., Sarfaraz Ahmed Shaikh, Abdul Salam Lodhi and Ghulam Jilani. (2015). ARIMA Modelling for Forecasting of Rice Production: A Case Study of Pakistan. Lasbela, U. J.Sci. Techl., vol.IV , pp. 117-120, 2015.
    連結:
  14. Lee, M. H., et al. (2012). "Seasonal ARIMA for forecasting air pollution index." American Journal of Applied Sciences 9(4): 570-578.
    連結:
  15. Lim, C., & McAleer, M. (2002). Time series forecasts of international travel demand for Australia. Tourism Management, 23(4), 389-396.
    連結:
  16. M. Amin, M. A. a. A. A. (2014). Time series modeling for forecasting wheat production of pakistan. Journal of Animal & Plant Sciences, 24(5).
    連結:
  17. Rahman, N. M. F. (2010). Forecasting Of Boro Rice Production in Bangladesh: An ARIMA Approach. Journal of Bangladesh Agril. Univ. 8(1): 103–112.
    連結:
  18. Ray, W. (1982). ARIMA forecasting models in inventory control. Journal of the Operational Research Society, 33(6), 567-574.
    連結:
  19. Romprasert, S. (2013). Thailand’s pineapple juice export value. Journal of Business Administration, 9, 1-9.
    連結:
  20. Sbrana, G., & Silvestrini, A. (2014). Random switching exponential smoothing and inventory forecasting. International Journal of Production Economics, 156, 283-294.
    連結:
  21. Sivapathasundaram, V., & Bogahawatte, C. (2012). Forecasting of paddy production in Sri Lanka: a time series analysis using ARIMA model.
    連結:
  22. Sopipan, N. (2014). "Forecasting rainfall in Thailand: A case study of Nakhon Ratchasima Province." Int. J. Environ. Ecol. Geol. Geophys. Eng 8: 717-721.
    連結:
  23. Taneja, K., et al. (2016). "Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach." Atmospheric Pollution Research 7(4): 585-596.
    連結:
  24. Taylor, J. W. (2003). "Short-term electricity demand forecasting using double seasonal exponential smoothing." Journal of the Operational Research Society 54(8): 799-805.
    連結:
  25. Tran, V. G., et al. (2012). One week hourly electricity load forecasting using neuro-fuzzy and seasonal ARIMA models. Power Plants and Power Systems Control.
    連結:
  26. Udom, P. and N. Phumchusri (2014). "A comparison study between time series model and ARIMA model for sales forecasting of distributor in plastic industry." IOSR Journal of Engineering 4: 32-38.
    連結:
  27. Valipour, M. (2015). "Long‐term runoff study using SARIMA and ARIMA models in the United States." Meteorological Applications 22(3): 592-598.
    連結:
  28. Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2013). Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of hydrology, 476, 433-441
    連結:
  29. Wang, C.-C. (2011). A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export. Expert Systems with Applications, 38(8), 9296-9304.
    連結:
  30. Wang, Y., et al. (2012). "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China." Energy Policy 48: 284-294.
    連結:
  31. Williams, B. M. and L. A. Hoel (2003). "Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results." Journal of transportation engineering 129(6): 664-672.
    連結:
  32. Zhang, G. P. (2003). "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50: 159-175.
    連結:
  33. Zhou, Z.-b., & Dong, X.-c. (2012). Analysis about the seasonality of China's crude oil import based on X-12-ARIMA. Energy, 42(1), 281-288.
    連結:
  34. Abdulla, M. M. H. a. F. (2015). A Time Series Analysis for the Pineapple Production in Bangladesh. Journal of ScienceVol. 38, No. 2, pp. 49-59.
  35. Adejumo, A. O. a. M., A. A. (2013). Modeling Box-Jenkins Methodology on Retail Prices of Rice in Nigeria. International Journal Of Engineering And Science (IJES), ISSN(e): 2319 – 1813 ISSN(p): 2319 – 1805.
  36. Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques.
  37. As' ad, M. (2012). "Finding the best ARIMA model to forecast daily peak electricity demand."
  38. Asteriou, D. and S. G. Hall (2015). Applied econometrics, Palgrave Macmillan.
  39. Asteriou, S. G. H. D. (2011). Applied Econometrics (2nd ed.). New York: Palgrave Macmillan.
  40. Bhattacharyya, R. B. a. B. (2013). ARIMA modeling to forecast area and production of rice in West Bengal. Journal of Crop and Weed, pp. 1-31.
  41. Biswas, R., & Bhattacharyya, B. (2013). ARIMA modeling to forecast area and production of rice in West Bengal. Journal of Crop and Weed, 9(2), 26-31.
  42. center, K. b. r. (2016). Thai's processed food export. Retrieved from www.kasikornbank.com/TH/SME/.../ExportProcessedFood.pdf
  43. Cho, M., Hwang, J., & Chen, C. (1995). Customer short term load forecasting by using ARIMA transfer function model. Paper presented at the Energy Management and Power Delivery, 1995. Proceedings of EMPD'95., 1995 International Conference on.
  44. Economics, O. oF A. (2015). Thailand’s Agricultural Products report 2015.
  45. Bangkok.
  46. Foreman, S. E. (1993). "An application of Box-Jenkins ARIMA techniques to airline safety data." Logistics and Transportation Review 29(3): 221.
  47. Gerolimetto, M. (2010). "ARIMA and SARIMA models." Ca’Foscari University of Venice, Italy.
  48. Govardhana Rao, G., 2Solmonrajupaul, K., 3Vishnu Sankarrao, D. and 4Dayakar, G. (2014). Seasonal Variations And Forecasting In Wholesale Prices Of Rice (Paddy) In Guntur District Of Andhra Pradesh. International Journal of Development Research,Vol. 4, Issue, 11, pp. 2418-2422.
  49. Hamjah, M. A. (2014). Forecasting Major Fruit Crops Productions in Bangladesh using Box-Jenkins ARIMA Model. Journal of Economics and Sustainable Development.
  50. Hyndman, R. J. and A. V. Kostenko (2007). "Minimum sample size requirements for seasonal forecasting models." Foresight 6(Spring): 12-15.
  51. Kalekar, P. S. (2004). "Time series forecasting using holt-winters exponential smoothing." Kanwal Rekhi School of Information Technology 4329008: 1-13.
  52. Mekparyup, J. and K. Saithanu (2015). "A seasonal ARIMA model for forecasting the dengue hemorrhagic fever patients in Rayong, Thailand." Global Journal of Pure and Applied Mathematics 11(2): 175-181.
  53. Nochai, R. and T. Nochai (2006). ARIMA model for forecasting oil palm price. Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications.
  54. Omane-Adjepong, M., et al. (2013). "Determining the better approach for short-term forecasting of ghana’s inflation: Seasonal ARIMA Vs holt-winters." International Journal of Business, Humanities and Technology 3(1): 69-79.
  55. Pankratz, A. (2009). Forecasting with univariate Box-Jenkins models: Concepts and cases (Vol. 224): John Wiley & Sons.
  56. Pattranurakyothin, T. and K. Kumnungkit (2012). "Forecasting Model for Para Rubber’s Export Sales." KMITL Sci. Technol. J 12(2): 198-202.
  57. Promprou, S., et al. (2006). "Forecasting dengue haemorrhagic fever cases in Southern Thailand using ARIMA Models." Dengue Bulletin 30: 99.
  58. Rangsan Nochai, T. N. (2006). ARIMA model for forecasting oil palm price. Paper presented at the proceedings of the 2nd IMT-GT regional conference of mathematics, statistics and applications, Universiti Sains Malaysia.
  59. Sriwichailamphan, T., Sriboonchitta, S., Wiboonpongse, A., & Chaovanapoonphol, Y. (2007). Factors affecting good agricultural practice in pineapple farming in Thailand. Paper presented at the II International Symposium on Improving the Performance of Supply Chains in the Transitional Economies 794.
  60. Tsay, R. S. (2005). Analysis of financial time series, John Wiley & Sons.