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

Abstract

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

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.

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References 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

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

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