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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94765| 標題: | 國產花椰菜價格分析:時間序列與機器學習模型之應用 Price Analysis of Domestic Produced Cauliflower : Time Series and Machine Learning Models Approach |
| 作者: | 林彥齊 Yan-Qi Lin |
| 指導教授: | 陳郁蕙 Yu-Hui Chen |
| 關鍵字: | 花椰菜,價格分析,ARIMA模型,隨機森林模型, cauliflower,price analysis,ARIMA model,Random Forest model, |
| 出版年 : | 2024 |
| 學位: | 碩士 |
| 摘要: | 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。 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. |
| URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94765 |
| DOI: | 10.6342/NTU202402940 |
| 全文授權: | 同意授權(限校園內公開) |
| 電子全文公開日期: | 2025-07-31 |
| 顯示於系所單位: | 農業經濟學系 |
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| ntu-112-2.pdf 授權僅限NTU校內IP使用(校園外請利用VPN校外連線服務) | 4.46 MB | Adobe PDF |
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