基于时间序列分析模型的贵州省金针菇价格预测研究

doi:10.19788/j.issn.2096-6369.000118

农业大数据学报 ›› 2026, Vol. 8 ›› Issue (2): 163-173.doi: 10.19788/j.issn.2096-6369.000118

基于时间序列分析模型的贵州省金针菇价格预测研究

付芳婧¹^,²^,^*(), 唐辟如¹^,^*(), 崔蕾¹, 冷海燕²

¹ 贵州省生态与农业气象中心，贵阳 550081
² 贵州省气象灾害防御中心，贵阳 550081

收稿日期:2026-01-22 接受日期:2026-07-24 出版日期:2026-06-05 发布日期:2026-06-26
通讯作者: 付芳婧，E-mail：254664090@qq.com；
唐辟如，E-mail：350337780@qq.com。
基金资助:
贵州省气象局省市联合基金项目《贵州省辣椒价格预测模型研究》(黔气科合SS[2023]19号);贵州省气象局省市联合基金项目《贵州省粮油类产品价格预警指标研究》(黔气科合SS[2023]18号);贵州省气象局创新团队“农业气象服务关键技术研究”(黔气科合TD〔2024〕6号)

Research on the Prediction of Enoki Mushroom Prices in Guizhou Province Based on Time Series Analysis Models

FU FangJing¹^,²^,^*(), TANG BiRu¹^,^*(), CUI Lei¹, LENG HaiYan²

¹ Guizhou Provincial Ecological and Agricultural Meteorological Center, Guiyang 550081, China
² Guizhou Meteorological Disaster Prevention Center, Guiyang 550081, China

Received:2026-01-22 Accepted:2026-07-24 Published:2026-06-05 Online:2026-06-26

摘要/Abstract

摘要：

精确的短期价格预测对于我国食用菌类市场至关重要，而常见食用菌类价格波动对菇农的收入和生活质量产生了深远影响。本研究以贵州省金针菇价格为例，应用了ARIMA模型、AR模型和STL季节性分解方法，基于贵州省金针菇历史价格数据，通过探索性数据分析、参数优化及模型验证，深入探究了气候、季节及经济指标对价格的影响。实证分析结果显示：所建模型能够准确预测食用菌价格走势，为市场供需调节和生产规划提供了科学依据，促进了食用菌产业的健康发展与农民增收。

关键词: 金针菇价格, 时间序列, ARIMA模型, STL季节性分解方法

Abstract:

Accurate short-term price forecasting is crucial for the edible mushroom market in our country, and the fluctuation of common edible mushroom prices has a profound impact on the income and quality of life of mushroom farmers. This study takes the price of golden mushrooms in Guizhou Province as an example, applies the ARIMA model, AR model, and STL seasonal decomposition method, based on the historical price data of golden mushrooms in Guizhou Province, through exploratory data analysis, parameter optimization, and model verification, it deeply explores the impact of climate, season, and economic indicators on prices. The empirical analysis results show that the model built in this study can accurately predict the trend of edible mushroom prices, providing scientific basis for market supply and demand regulation and production planning, and promoting the healthy development of the edible mushroom industry and the increase of farmers' income.

Key words: enoki mushroom price, time series, ARIMA model, STL seasonal decomposition approach

付芳婧, 唐辟如, 崔蕾, 冷海燕. 基于时间序列分析模型的贵州省金针菇价格预测研究[J]. 农业大数据学报, 2026, 8(2): 163-173.

FU FangJing, TANG BiRu, CUI Lei, LENG HaiYan. Research on the Prediction of Enoki Mushroom Prices in Guizhou Province Based on Time Series Analysis Models[J]. Journal of Agricultural Big Data, 2026, 8(2): 163-173.

图/表 9

图1

图2

图3

图4

图5

图6

图7

表1

表2

参考文献 22

[1]	管炜, 黄伟, 许国莲. 基于食用菌产业服务乡村振兴的运营模式分析. 中国食用菌, 2021, 40(7): 84-91+95.
	GUAN W, HUANG W, XU G L. Analysis on the operationg model based on the edible fungi industry serving rural revitalization. Edible Fungi of China, 2021, 40(7):84-91+95.
[2]	李墨卿. 食用菌价格、产量波动及影响因素实证分析——以福建尤溪县为例. 福建农业科技, 2024, 55(3): 70-78.
	LI M Q. Empirical analysis of price, yield fluctuations, and influencing factors of edible fungi: A csse study of Youxi County, Fujian Province. Fujian Agricultural Science and Technology, 2024, 55(3):70-78.
[3]	刘晓庆, 张宇萌. 基于ARIMA模型的食用菌价格预测分析——以江苏省杏鲍菇为例. 生态经济(学术版), 2012(2): 298-300.
	LIU X Q, ZHANG Y M. Edible fungus price forecast analysis based on the ARIMA model: To boris apricot mushroom in Jiangsu Province as an example. Industry Survey, 2012(2):298-300.
[4]	刘勇. 基于深度学习的蔬菜价格预测模型研究. 黑龙江科学, 2025, 16(12): 100-102.
	LIU Y. Research on vegetable price prediction model based on deep learning. Heilongjiang Science, 2025, 16(12):100-102.
[5]	王子豪, 白丽, 张润清, 等. 河北省食用菌价格波动特征及预测分析. 河北农业大学学报(社会科学版), 2019, 21(2): 1 1-18.
	WANG Z H, BAI L, ZHANG R Q, et al. Price fluctuation characteristics and forecast analysis of edible fungi in Hebei Province. Journal of Hebei Agricultural University(Social Sciences),. 2019, 21(2): 11-18.
[6]	赵梅, 刘维忠. ARIMA和平滑ARIMA模型的中国棉花价格短期预测比较. 贵州农业科学, 2015, 43(11): 206-208.
	ZHAO M, LIU W Z. Comparison of short-term cotton price forecasted by ARIMA model and Smooth ARIMA model in China. Guizhou Agricultural Sciences,. 2015, 43(11):206-208.
[7]	赵忠玲, 王笑. 基于神经网络的食用菌价格预测模型的建立. 中国食用菌, 2020, 39(3): 168-170.
	ZHAO Z L, WANG X. Establishment of prediction model of edible funing price based on Neural Network. Edible Fungi of China,. 2020, 39(3):168-170.
[8]	董永刚. 基于蛛网模型的食用菌价格波动预测——以平菇为例. 中国食用菌, 2019, 38(7): 128-132.
	DONG Y G. Price fluctuation forecast of edible mushrooms based on the Cobweb model: A case study of Oyster mushrooms. Edible Fungi of China, 2019, 38(7): 128-132.
[9]	朱婧, 范亚东, 徐勇. 基于改进GM(1,1)模型的中国大豆价格预测. 大豆科学, 2016, 35(2): 315-319.
	ZHU J, FAN Y D, XU Y. Soybean price prediction in China based on modified GM(1,1) model. Soybean Science, 2016, 35(2): 315-319.
[10]	刘石磊. 国际国内玉米价格联动因素分析. 中国国情国力, 2024(7): 77-79.
	LIU S L. Analysis of factors influencing the linkage between international and domestic corn prices. China National Conditions and Strength, 2024(7): 77-79.
[11]	张默涵, 王晓东, 王军, 等. 十年来北京市食用菌市场价格波动特征. 中国食用菌, 2023, 42(5): 105-115.
	ZHANG M H, WANG X D, WANG J, et al. The fluctuation characteristics of edible fungi market price in Beijing during the past ten years. Edible Fungi of China, 2023, 42(5): 105-115.
[12]	宋晨龙, 曹健. 我国棉花价格波动影响因素分析. 合作经济与科技, 2024(8): 82-85.
	SONG C L, CAO J. Analysis of factors influencing fluctuations in China's cotton prices. CO-Oerativeconomy & Science, 2024(8): 82-85.
[13]	王芳, 孙晓红, 陶光灿. 贵州省食用菌产业发展状况及对策. 贵州农业科学, 2020, 48(12): 77-81.
	WANG F, SUN X H, TAO G C. Development situation and countermeasures of edible fungus industry in Guizhou province. Guizhou Agricultural Sciences, 2020, 48(12): 77-81.
[14]	杨锌沂, 周韬, 赵光程. 贵州省食用菌产业发展现状与发展对策. 耕作与栽培, 2022, 42(4): 150-151.
	YANG Z Y, ZHOU T, ZHAO G C. Development status and countermeasures of edible fungi industry in Guizhou province. Tillage and Cultivation, 2022, 42(4): 150-151.
[15]	赵湘江, 杨兰. 贵州省食用菌产业发展研究. 中国食用菌, 2021, 40(4): 117-122+126.
	ZHAO X J, YANG L. Developmental research on edible fungi industry in Guizhou province. Edible Fungi of China, 2021, 40(4): 117-122+126.
[16]	KUNANDAR P, DINDY D P, IRENE K E W, et al. Forecasting of red chilli prices in banyumas regency: The ARIMA approach. E3S Web of Conferences, 2023, 444: 02017. DOI: 10.1051/e3sconf/202344402017.
[17]	WANG X Q, KANG Y F, HYNDMAN R J, et al. Distributed ARIMA models for ultra-long time series. International Journal of Forecasting, 2023, 39(3): 1163-1184. doi: 10.1016/j.ijforecast.2022.05.001
[18]	谭巧巧, 杨吉云. 改进的自回归AR(p)预测模型在WSN中的应用. 计算机工程与应用, 2015, 51(12): 83-87.
	TAN Q Q, YANG J Y. Application of improved AR(p) prediction model in WSN. Computer Engineering and Applications, 2015, 51(12): 83-87.
[19]	MA Q D, DU X, LIU C, et al. A hybrid deep learning method for the prediction of ship time headway using automatic identification system data. Engineering Applications of Artificial Intelligence, 2024, 133: 108172. doi: 10.1016/j.engappai.2024.108172
[20]	GUO Y, JIA M J, SU C, et al. A novel CALA-STL algorithm for optimizing prediction of building energy heat load. Energy and Buildings, 2025, 328: 115207. doi: 10.1016/j.enbuild.2024.115207
[21]	CHEN Y. A novel method for corn futures price prediction integrating decomposition, denoising, feature selection and hybrid networks. Annals of Operations Research, 2025, 353(2): 449-484. doi: 10.1007/s10479-025-06525-8
[22]	SAMEE N A, ATTEIA G, ALKANHEL R, et al. Hybrid feature reduction using PCC-stacked autoencoders for gold/oil prices forecasting under COVID-19 pandemic. Electronics, 2022, 11(7): 991. DOI:10.3390/electronics11070991.

时间	实际值	ARIMA		AR		STL
时间	实际值	预测值	误差	预测值	误差	预测值	置信区间	误差
2024年1月	7.6	7.5	0.1	7.6	0	7.6	±0.50	0
2024年2月	7.7	7.6	0.1	7.5	0.2	7.4	±0.51	0.3
2024年3月	7.2	7.7	0.5	7.2	0	7.3	±0.62	0.1
2024年4月	6.9	7.9	1.0	6.8	0.1	7.1	±0.69	0.2
2024年5月	7.1	8.0	0.9	6.5	0.6	7.0	±0.74	0.1
2024年6月	7.1	8.1	1.0	6.2	0.9	6.8	±0.77	0.3
2024年7月	7.0	8.1	0.9	6.0	1.0	6.5	±0.80	0.5
2024年8月	6.9	8.0	1.1	6.0	0.9	6.4	±0.81	0.5
2024年9月	7.1	7.9	0.8	6.1	1.0	6.2	±0.82	0.9
2024年10月	7.0	7.7	0.7	6.1	0.9	6.2	±0.83	0.8
2024年11月	7.0	7.6	0.6	6.1	0.9	6.3	±0.83	0.7
2024年12月	6.9	7.6	0.7	6.1	0.8	6.4	±0.84	0.5
2025年1月	7.2	7.5	0.3	6.8	0.4	6.8	±0.84	0.4
2025年2月	7.4	7.6	0.2	6.8	0.6	7.1	±0.84	0.3
2025年3月	6.9	6.9	0	6.5	0.4	6.4	±0.84	0.5
2025年4月	6.7	6.9	0.2	6.3	0.4	6.2	±0.84	0.5
2025年5月	6.5	7.2	0.7	7.1	0.6	6.5	±0.84	0
2025年6月	6.5	7.2	0.7	7	0.5	6.5	±0.84	0
2025年7月		7.1		7.1		6.5	±0.84
2025年8月		7.1		7.2		6.9	±0.84
2025年9月		7		7.2		7.1	±0.84
2025年10月		7		7.1		7.1	±0.84

	RMSE	MAPE (%)
ARIMA	0.692	8.55
AR	0.656	8.12
STL	0.452	5.22

基于时间序列分析模型的贵州省金针菇价格预测研究

Research on the Prediction of Enoki Mushroom Prices in Guizhou Province Based on Time Series Analysis Models

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 22

相关文章 2

Metrics

本文评价

推荐阅读 0

[1]	轩彤, 修子涵, 初洪龙. Ensemble融合模型的建立及其在面粉价格预测中的应用[J]. 农业大数据学报, 2026, 8(1): 36-47.
[2]	张晓艳, 孟丽, 王丽丽, 刘锋, 刘佳佳, 卢德成. 基于大数据的我国小麦市场价格波动特点分析——以山东省为例[J]. 农业大数据学报, 2020, 2(3): 75-83.