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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳郁蕙 | zh_TW |
| dc.contributor.advisor | Yu-Hui Chen | en |
| dc.contributor.author | 林彥齊 | zh_TW |
| dc.contributor.author | Yan-Qi Lin | en |
| dc.date.accessioned | 2024-08-19T16:12:05Z | - |
| dc.date.available | 2024-08-20 | - |
| dc.date.copyright | 2024-08-19 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-31 | - |
| dc.identifier.citation | 丁介郁，邱貿凱，官俊榮，(2014) 氣候條件對於臺灣蔬菜批發價格之影響，台灣農學會報，15(4)，422-448。
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Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one, 13(3), e0194889. Vikram, R., Divij, R., Hishore, N., Naveen, G., & Rudhramoorthy, D. (2021). Crop price prediction using machine learning naive Bayes algorithms. In International Conference on Ubiquitous Computing and Intelligent Information Systems (pp. 27-34). Singapore: Springer Nature Singapore. Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A comparison between arima, lstm, and gru for time series forecasting. In Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence, 49-55. Yanli, L., Wang, Y., & Zhang, J., 2012. New Machine Learning Algorithm: Random Forest, ICICA, Page 246-252. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94765 | - |
| dc.description.abstract | 大宗蔬菜在批發市場中交易量最大，其價格會影響批發市場的平均價格行情，故分析大宗蔬菜價格有其重要性。在三種大宗蔬菜(甘藍、包心白菜及花椰菜)中，國內價格相關研究對前兩者著墨較多，反觀花椰菜的價格分析是較少被討論到的，過去與花椰菜相關的研究主要聚焦在生長或品種改良、研發上，極少分析花椰菜批發市場價格及影響價格因素的研究。因此本研究目的在於瞭解我國花椰菜的生產背景與在果菜批發市場價格及交易情況，並分析影響花椰菜價格的可能因素。本文以國內已公開的十七處果菜批發市場資料為基礎，彙整分析 2012 - 2022 年的樣本期間全部市場、產地市場及消費地市場的交易量及價格資料，利用時間序列的 ARIMA 模型及隨機森林模型作估計，分析影響花椰菜價格之因素與其影響力高低。研究發現花椰菜價格具季節性，在十一月至四月之間價格較低，但五月到十月的價格較高；在研究期間因產量、產值、種植面積呈衰退情況，使得花椰菜價格有上漲的趨勢。在影響價格因素部分，值花椰菜價格受到前 1 - 2 旬價格及交易量的影響最大；另外兩種大宗蔬菜(甘藍與包心白菜)的前一期交易價格及交易量也會影響到花椰菜當期價格，而甘藍的影響力明顯高過於包心白菜；不過 COVID-19 疫情與四大節慶則對花椰菜價格無顯著影響。 | zh_TW |
| dc.description.abstract | Major vegetables account for the highest transaction volume in the wholesale market, and their prices influence the average market price trends. Therefore, analyzing the prices of major vegetables is of significant importance. Among the three major vegetables (Taiwanese cabbage, napa cabbage, and cauliflower), domestic price-related research has focused more on the former two, while price analysis of cauliflower has been less discussed. Previous studies related to cauliflower mainly focused on growth, variety improvement, and R&D, with very few studies analyzing wholesale market prices and the factors influencing cauliflower prices. Therefore, the purpose of this study is to understand the production background of cauliflower in Taiwan, its price and trading situation in the fruit and vegetable wholesale market, and to analyze the potential factors influencing cauliflower prices.This paper is based on publicly available data from seventeen fruit and vegetable wholesale markets in Taiwan, consolidating and analyzing transaction volume and price data from all markets, production markets, and consumption markets during the sample period from 2012 to 2022. Using time series ARIMA models and Random Forest models for estimation, it analyzes the factors influencing cauliflower prices and their impact levels. The study finds that cauliflower prices exhibit seasonality, with lower prices from November to April and higher prices from May to October. During the study period, the decline in production volume, production value, and planting area has led to an upward trend in cauliflower prices.Regarding the factors influencing prices, cauliflower prices are most significantly affected by the prices and transaction volumes of the previous 1-2 ten-day periods. Additionally, the previous transaction prices and volumes of the other two major vegetables (Taiwanese cabbage and napa cabbage) also influence the current price of cauliflower, with the influence of Taiwanese cabbage being significantly greater than that of napa cabbage. However, the COVID-19 pandemic and the four major festivals did not have a significant impact on cauliflower prices. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-19T16:12:05Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-19T16:12:05Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝 辭 i
摘 要 ii Abstract iii 目 次 iv 圖 次 vi 表 次 viii 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究步驟與論文架構 3 第二章 國產花椰菜產業概況及價格 5 第一節 國內花椰菜生產概況 5 第二節 國內花椰菜交易概況 22 第三節 小結 41 第三章 文獻回顧 42 第一節 農產品價量分析文獻 42 第二節 預測模型比較相關研究 52 第三節 小結 55 第四章 研究方法 56 第一節 AIRMA模型 56 第二節 隨機森林模型 60 第五章 實證分析 64 第一節 資料來源 64 第二節 實證分析結果 69 第六章 結論與建議 99 第一節 結論 99 第二節 建議 101 參考文獻 102 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 國產花椰菜價格分析：時間序列與機器學習模型之應用 | zh_TW |
| dc.title | Price Analysis of Domestic Produced Cauliflower : Time Series and Machine Learning Models Approach | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 詹滿色;鄭佳宜 | zh_TW |
| dc.contributor.oralexamcommittee | Man-ser Jan;Chia-Yi Cheng | en |
| dc.subject.keyword | 花椰菜,價格分析,ARIMA模型,隨機森林模型, | zh_TW |
| dc.subject.keyword | cauliflower,price analysis,ARIMA model,Random Forest model, | en |
| dc.relation.page | 108 | - |
| dc.identifier.doi | 10.6342/NTU202402940 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-02 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 農業經濟學系 | - |
| dc.date.embargo-lift | 2025-07-31 | - |
| 顯示於系所單位： | 農業經濟學系 | |
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