dc.contributor.advisor | 張逸民 | zh_TW |
dc.contributor.advisor | Chang, Yegming | en_US |
dc.contributor.author (作者) | 潘家鋒 | zh_TW |
dc.contributor.author (作者) | Pan, Jason | en_US |
dc.creator (作者) | 潘家鋒 | zh_TW |
dc.creator (作者) | Pan, Jason | en_US |
dc.date (日期) | 2010 | en_US |
dc.date.accessioned | 5-十月-2011 14:29:05 (UTC+8) | - |
dc.date.available | 5-十月-2011 14:29:05 (UTC+8) | - |
dc.date.issued (上傳時間) | 5-十月-2011 14:29:05 (UTC+8) | - |
dc.identifier (其他 識別碼) | G0098355070 | en_US |
dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/51189 | - |
dc.description (描述) | 碩士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 企業管理研究所 | zh_TW |
dc.description (描述) | 98355070 | zh_TW |
dc.description (描述) | 99 | zh_TW |
dc.description.abstract (摘要) | The trend seasonal demand pattern is encountered when both trend and seasonal influences are interactive. The problem of this research is to project the seasonal market sales using ice cream and fresh milk in Taiwan as examples. In order to improve the accuracy of forecast, two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. In this study, we present two forecasting models used for evaluation to predict seasonal market sales of ice cream, fresh milk, and air conditioner in Taiwan. It includes Winters multiplicative seasonal trend model and the Decomposition method. Two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. After the validation process, Winters multiplicative seasonal trend model is selected based on the minimum MSE, and the monthly sales forecast for the year of 2011 is conducted using the data(60 months). Number Cruncher Statistical System (NCSS) is used for analyzing the data which proves useful and powerful. In summary, the results demonstrate that Winters multiplicative seasonal trend model has the smallest mean square error in this case. Therefore, we conclude that both Winters multiplicative seasonal trend model and the Decomposition model are well fitted for forecasting the seasonal market sales. Yet, Winters multiplicative seasonal trend model is the better method to be used in this study since it generates the smallest mean square error (MSE) during the period of validation. | en_US |
dc.description.tableofcontents | TABLE OF CONTENTSACKNOWLEDGEMENTS................. iABSTRACT......................... iiTABLE OF CONTENTS................ iiiTABLES........................... vFIGURES.......................... viiCHAPTER1Introduction............. 11.1 Problem Statement............ 21.2 Research Objectives.......... 31.3 Research Data................ 31.4 Organization of the Thesis... 4CHAPTER 2 Literature Review...... 5CHAPTER 3Two Forecasting Models for the Seasonal Demand........................... 123.1 Winters Multiplicative Trend Seasonal Model............................ 123.2 Decomposition Forecasting.... 163.3 Estimation and Validation.... 213.4 Forecasting Accuracy......... 213.5 Software used in the research 22CHAPTER 4Data Collection and Analysis......................... 234.1 Data......................... 234.2 Results of Forecasting for the Monthly Sales of Ice Cream............................ 254.2.1 Validation................. 254.2.2 Forecasts.................. 344.3 Results of Forecasting for the Monthly Sales of Fresh Milk............................. 374.3.1 Validation ................ 374.3.2 Forecasts.................. 474.4 Results of Forecasting for the Monthly Sales of Air Conditioner...................... 514.4.1 Validation ................ 514.4.2 Forecasts.................. 60CHAPTER 5Conclusion, Implications, and Future Research ........................ 655.1 Conclusions ................. 655.2 Implications ................ 665.3 Research Limitations ........ 665.4 Future Research ............. 67References....................... 69Appendix A....................... 73Appendix B ...................... 76Appendix C ...................... 80Appendix D....................... 83Appendix E ...................... 86Appendix F....................... 91Appendix G....................... 94Appendix H....................... 98Appendix I ...................... 104 | zh_TW |
dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0098355070 | en_US |
dc.subject (關鍵詞) | 季節性消費品 | zh_TW |
dc.subject (關鍵詞) | 銷售量預測 | zh_TW |
dc.subject (關鍵詞) | MSE | zh_TW |
dc.subject (關鍵詞) | sales forecast | en_US |
dc.subject (關鍵詞) | seasonal consumer products | en_US |
dc.subject (關鍵詞) | winters | en_US |
dc.subject (關鍵詞) | decomposition | en_US |
dc.subject (關鍵詞) | mean square error | en_US |
dc.subject (關鍵詞) | NCSS | en_US |
dc.title (題名) | 台灣季節性消費品銷售預測之研究 | zh_TW |
dc.title (題名) | The investigation of forecasting models for the sales of seasonal consumer products in Taiwan | en_US |
dc.type (資料類型) | thesis | en |
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