基于ARIMA模型对陕西省苹果产量的分析与预测
Analysis and Prediction of Apple Yield in Shaanxi Province Based on ARIMA Model
摘要: 苹果含有碳水化合物、维生素以及微量元素多种营养物质,有利于儿童的生长发育,增强记忆力,可以说营养价值在众多水果中堪称完美。在我国的苹果种植区内,陕西省是适合生产的最佳地区,并且随着时间的增长,陕西省苹果产量呈现出逐渐上升的状态,其产量位居首位。以统计学、经济学为基础,采用ARIMA模型对陕西省苹果产量进行了分析预测研究。对陕西统计年鉴40年间陕西省苹果产量统计数据做统计分析,依据求和自回归移动平均模型理论,运用Eviews软件通过平稳检验、白噪声检验、模型识别、参数估计、模型的检验与优化等一系列的过程,最终建立ARIMA(2,1,1)模型。利用建立的模型对陕西省近3年的苹果产量进行了预测比较,对模型进行评估,其平均相对误差在6.61%,可以较好的反映陕西省苹果产量的发展趋势。使用ARIMA模型分析苹果产量等时间序列数据,可以利用其优势来预测未来的产量趋势,这对于农业生产规划和市场分析具有重要的实际应用价值。
Abstract: Apples contain carbohydrates, vitamins and trace elements that are beneficial for children’s growth and memory; in other words, their nutritional value is perfect among many fruits. In China’s apple planting area, Shaanxi Province is suitable for production of the best area, and with the growth of time, Shaanxi Province apple output shows a state of gradual rise. Its output ranks the first. Based on statistics and economics, ARIMA model was used to analyze and forecast the apple yield in Shaanxi Province. The statistical data of apple yield in Shaanxi Province in the past 40 years of Shaanxi Statistical Yearbook were analyzed. Based on the theory of autoregressive moving average model, the ARIMA(2,1,1) model was finally established by using Eviews through a series of processes, such as stationary test, white noise test, model recognition, parameter estimation, model test and optimization. The established model is used to forecast and compare the apple yield of Shaanxi Province in the past three years. The average relative error of the model is 6.61%, which can better reflect the development trend of apple yield in Shaanxi Province and make short-term forecast. On the basis of the above, some suggestions on improving apple yield in Shaanxi province are put forward.
参考文献
|
[1]
|
任嘉颖. 山西运城苹果产量预测及影响因素分析[D]: [硕士学位论文]. 晋中: 山西农业大学, 2017.
|
|
[2]
|
赵嘉宝, 陈杰, 安霞, 孙占海, 张学东. 基于ARIMA模型的吐鲁番市葡萄产量预测分析[J]. 江苏科技信息, 2019, 36(31): 34-39.
|
|
[3]
|
阴明哲, 李一帆, 芦铃元, 邢家轩, 崔永福. 基于ARIMA的河北省花生产量预测分析[J]. 中国集体经济, 2021(3): 15-16.
|
|
[4]
|
陈鼎玉, 万坚, 程瀚锋. 基于ARIMA模型的我国粮食产量预测[J]. 营销界, 2019(13): 95-96.
|
|
[5]
|
李娟丽, 许英. 基于ARIMA模型的新疆棉花产量的预测分析[J]. 教育现代化, 2018. 5(17): 149-150+155.
|
|
[6]
|
高蕾. 基于ARIMA模型的安徽省粮食产量预测研究[J]. 合肥学院学报(社会科学版), 2015, 32(5): 85-87.
|
|
[7]
|
刘立国, 姜健, 贾媛媛. 基于ARIMA模型的锦州市粮食产量预测研究[J]. 辽宁工业大学学报(社会科学版), 2014, 16(3): 18-19+34.
|