Vegetable Price Forecasting Based on ARIMA Model and Random Forest Prediction
DOI:
https://doi.org/10.54097/3afpgv27Keywords:
Vegetable Pricing Strategy, ARIMA Model, Random Forest Prediction.Abstract
Given the trouble that the freshness length of vegetable commodities is incredibly brief and the fine will deteriorate with the expansion in promoting time, we elevate our arithmetic processing based totally on the present records to remedy the fundamental coefficients; utilize the Pearson correlation coefficient evaluation to get the visualization of the warmness map and inside the relationship between the whole quantity of income of every class and the fee plus pricing relationship; due to a large amount of data, multi-feature modeling is proposed using random forests to merge several aspects while predicting revenues, which provides good resistance to noise for most datasets and is less likely to fall into overfitting. Then, the random forest model is applied to predict the revenue volume; the wholesale charges of saleable small products are predicted by the ARIMA model; the complete revenue corresponding to the constant revenue charge of each category is calculated; the procedure is optimized with the help of constraints; and finally, the multi-feature prediction of vegetable demand is made according to the random forest model, which in turn gives the replenishment and pricing strategy. Through the above analysis, vegetable supermarkets can make higher pricing and inventory choices to maximize profits.
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Wei, C.-C.; Chen, L.-T. Supply Chain Replenishment Decision for Newsvendor Products with Multiple Periods and a Short Life Cycle. Sustainability, 2021, 13, 12777.
Azevedo S G, Pimentel C M O, Alves A C, et al. Support of advanced technologies in supply chain processes and sustainability impact[J]. Applied Sciences, 2021, 11(7): 3026.
Jørgensen, S., & Kort, P. M. Optimal pricing and inventory policies: Centralized and decentralized decision-making. European Journal of Operational Research, 2002, 138(3), 578–600
Khorshidvand B, Soleimani H, Sibdari S, et al. Revenue management in a multi-level multi-channel supply chain considering pricing, greening, and advertising decisions[J]. Journal of Retailing and Consumer Services, 2021, 59: 102425.
LIU Jian-yi, LU Jiang, CHEN Yi-zhao, et al. Study on prediction model of liquid holds up based on random forest algorithm[J]. Chemical Engineering Science, 2023, 268: 118383.
BERTIN Takoutsing, GERARD B.M. Heuvelink. Comparing the prediction performance, uncertainty quantification, and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors[J]. Geoderma, 2022, 428: 116192.
MICHAEL Parzinger, LUCIA Hanfstaengl, FERDINAND Sigg, et al. Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection using machine learning methods Random Forest and LASSO[J]. Building and Environment, 2022, 223: 109313.
GENG Pei, GONG Xiao-tao, CHEN Wen-jing, et al. Hot Deformation Behavior of Zr-2.5Nb[J]. Journal of Netshape Forming Engineering, 2022, 14(6): 65-70.
ZHOU Jian, DAI Yong, TAO Ming, et al. Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm[J]. Results in Engineering, 2023, 17: 100892.
LEO Breiman. Schapire. Random Forests[J]. Machine Learning, 2001,45: 5-32.
GAO Wei, XU Fan, ZHOU zhi-hua. Towards convergence rate analysis of random forests for classification[J]. Artificial Intelligence, 2022, 313: 103788.
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