臺灣博碩士論文加值系統

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。

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.

Acknowledgement i
摘要 ii
Abstract iii
Contents iv
List of Figure vi
List of Table vii
Chapter 1 Introduction 1
1.1 Problem statement 1
1.2 Research objective 2
1.3 Scope and limitation 2
1.4 Research Procedure 3
Chapter 2 Research background 4
2.1 production 4
2.1.1 Fresh Pineapple production 4
2.1.2 Canned Pineapple production 7
2.1.3 Production cost 8
2.2 Marketing 9
2.2.1 Price situation 9
2.2.2 Import situation 13
2.2.3 Export situation 15
2.3 Competitive situation 18
2.3.1 Competitive Advantage and Competitive disadvantage 18
2.3.2 Tax measures of importing countries 18
2.3.3 Major competitor in the world market 20
2.4 Data Analysis 21
Chapter 3 Literature Review 23
3.1 Literature Review 23
Chapter 4 Empirical Analysis 28
4.1 Time Series Analysis 28
4.1.1 Unit Root test 28
4.1.2 Autoregressive Integrated Moving Average (ARIMA) models 30
4.1.3 Seasonal ARIMA model 33
4.1.4 Box – Jenkins model selection 33
4.1.5 The Box – Jenkins approach step by step 35
4.2 Data and Variables 35
4.3 The Methodology Process 36
Chapter 5 Empirical Results 37
5.1 Stationary checking 37
5.1.1 Unit Root Test 37
5.1.2 The ACF and PACF of the raw data 38
5.2 Identify model (Identification) 39
5.2.1 Model of export to united states 39
5.2.2 Model of export to European Union 39
5.3 Estimation 40
5.4 Forecasting 41
Chapter 6 Conclusion and Suggestion 42
6.1 Conclusion 42
6.2 Suggestion 42
Reference 44
Appendix 48

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.
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.
Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques.
Amin, M., Amanullah, M., & Akbar, A. (2014). Time Series Modeling for forecasting wheat production of Pakistan. Plant Sciences, 24(5), 1444-1451.
As' ad, M. (2012). "Finding the best ARIMA model to forecast daily peak electricity demand."
Asteriou, D. and S. G. Hall (2015). Applied econometrics, Palgrave Macmillan.
Asteriou, S. G. H. D. (2011). Applied Econometrics (2nd ed.). New York: Palgrave Macmillan.
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.
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.
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.
center, K. b. r. (2016). Thai's processed food export. Retrieved from www.kasikornbank.com/TH/SME/.../ExportProcessedFood.pdf
Chaiboonsri, P. B. S. K. C. and P. Chaitip (2009). "Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand."
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.
Cholette, P. A. (1982). Prior information and ARIMA forecasting. Journal of Forecasting, 1(4), 375-383.
Cho, V. (2003). "A comparison of three different approaches to tourist arrival forecasting." Tourism management 24(3): 323-330.
Chu, F.-L. (1998). "Forecasting tourism demand in Asian-Pacific countries." Annals of Tourism Research 25(3): 597-615.
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.
Economics, O. oF A. (2015). Thailand’s Agricultural Products report 2015.
Bangkok.
Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708.
Foreman, S. E. (1993). "An application of Box-Jenkins ARIMA techniques to airline safety data." Logistics and Transportation Review 29(3): 221.
Gerolimetto, M. (2010). "ARIMA and SARIMA models." Ca’Foscari University of Venice, Italy.
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.
Hamjah, M. A. (2014). Forecasting Major Fruit Crops Productions in Bangladesh using Box-Jenkins ARIMA Model. Journal of Economics and Sustainable Development.
Ho, S. and M. Xie (1998). "The use of ARIMA models for reliability forecasting and analysis." Computers & industrial engineering 35(1-2): 213-216.
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.
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.
Hyndman, R. J. and A. V. Kostenko (2007). "Minimum sample size requirements for seasonal forecasting models." Foresight 6(Spring): 12-15.
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.
Kalekar, P. S. (2004). "Time series forecasting using holt-winters exponential smoothing." Kanwal Rekhi School of Information Technology 4329008: 1-13.
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.
Lee, M. H., et al. (2012). "Seasonal ARIMA for forecasting air pollution index." American Journal of Applied Sciences 9(4): 570-578.
Lim, C., & McAleer, M. (2002). Time series forecasts of international travel demand for Australia. Tourism Management, 23(4), 389-396.
M. Amin, M. A. a. A. A. (2014). Time series modeling for forecasting wheat production of pakistan. Journal of Animal & Plant Sciences, 24(5).
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.
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.
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.
Pankratz, A. (2009). Forecasting with univariate Box-Jenkins models: Concepts and cases (Vol. 224): John Wiley & Sons.
Pattranurakyothin, T. and K. Kumnungkit (2012). "Forecasting Model for Para Rubber’s Export Sales." KMITL Sci. Technol. J 12(2): 198-202.
Promprou, S., et al. (2006). "Forecasting dengue haemorrhagic fever cases in Southern Thailand using ARIMA Models." Dengue Bulletin 30: 99.
Rahman, N. M. F. (2010). Forecasting Of Boro Rice Production in Bangladesh: An ARIMA Approach. Journal of Bangladesh Agril. Univ. 8(1): 103–112.
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.
Ray, W. (1982). ARIMA forecasting models in inventory control. Journal of the Operational Research Society, 33(6), 567-574.
Romprasert, S. (2013). Thailand’s pineapple juice export value. Journal of Business Administration, 9, 1-9.
Sbrana, G., & Silvestrini, A. (2014). Random switching exponential smoothing and inventory forecasting. International Journal of Production Economics, 156, 283-294.
Sivapathasundaram, V., & Bogahawatte, C. (2012). Forecasting of paddy production in Sri Lanka: a time series analysis using ARIMA model.
Sopipan, N. (2014). "Forecasting rainfall in Thailand: A case study of Nakhon Ratchasima Province." Int. J. Environ. Ecol. Geol. Geophys. Eng 8: 717-721.
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.
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.
Taylor, J. W. (2003). "Short-term electricity demand forecasting using double seasonal exponential smoothing." Journal of the Operational Research Society 54(8): 799-805.
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.
Tsay, R. S. (2005). Analysis of financial time series, John Wiley & Sons.
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.
Valipour, M. (2015). "Long‐term runoff study using SARIMA and ARIMA models in the United States." Meteorological Applications 22(3): 592-598.
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
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.
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.
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.
Zhang, G. P. (2003). "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50: 159-175.
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.