A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction
Source abstract
Abstract
Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Forecasting Techniques and Applications · Stock Market Forecasting Methods · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Business School, Yangzhou University, Yangzhou 225127, China
Funding
Yangzhou University High-Level Talent Research Initiation Project
References (42)
Show source-provided references
1. Forecasting fish prices with an artificial neural network model during the tuna fraud
2. Prediction of soybean price in China using QR-RBF neural network model10.1016/j.compag.2018.08.016 ↗
3. Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China10.1016/j.neucom.2017.11.053 ↗
4. Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting10.1016/j.eswa.2020.114232 ↗
5. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA10.1016/j.renene.2014.11.084 ↗
6. Time series forecasting using artificial neural networks methodologies: A systematic review10.1016/j.fcij.2018.10.003 ↗
7. Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression10.1016/j.jretconser.2016.05.008 ↗
8. A novel STL-based hybrid model for forecasting hog price in China10.1016/j.compag.2022.107068 ↗
9. Artificial neural network and wavelet neural network approaches for modelling of a solar air heater10.1016/j.eswa.2009.02.073 ↗
10. Time-delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India10.1007/s00521-012-1264-z ↗
11. Prediction for hog prices based on similar sub-series search and support vector regression10.1016/j.compag.2019.01.027 ↗
12. Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network10.1016/j.neunet.2019.01.003 ↗
13. Predicting E-commerce customer satisfaction: Traditional machine learning vs10.1016/j.jretconser.2024.103865 ↗
14. The Combination of Forecasts10.1057/jors.1969.103 ↗
15. Improving short term load forecast accuracy via combining sister forecasts10.1016/j.energy.2015.12.142 ↗
16. An adaptive hybrid model for short term electricity price forecasting10.1016/j.apenergy.2019.114087 ↗
17. Research on different weight combination in air quality forecasting models10.1016/j.jclepro.2020.121169 ↗
18. Artificial bee colony-based combination approach to forecasting agricultural commodity prices10.1016/j.ijforecast.2021.10.003 ↗
19. A feature-enhanced long short-term memory network combined with residual-driven ν support vector regression for financial market prediction10.1016/j.engappai.2022.105663 ↗
20. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm10.1016/j.neucom.2016.09.027 ↗
21. Prediction of particle mixing in rotary drums by a DEM data-driven PSO-SVR model10.1016/j.powtec.2024.119365 ↗
22. Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method10.1016/j.jclepro.2020.122248 ↗
23. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization10.1016/j.knosys.2021.107379 ↗
24. A novel swarm intelligence optimization approach: Sparrow search algorithm10.1080/21642583.2019.1708830 ↗
25. A stochastic configuration network based on chaotic sparrow search algorithm10.1016/j.knosys.2021.106924 ↗
26. Improving the accuracy of outlook price forecasts10.1111/j.1574-0862.2010.00519.x ↗
27. International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models10.1016/j.eneco.2010.04.009 ↗
28. Early forecasting of corn yield and price variations using satellite vegetation products10.1016/j.compag.2024.108962 ↗
29. Forecasting yield by integrating agrarian factors and machine learning models: A survey10.1016/j.compag.2018.10.024 ↗
30. Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices10.1016/j.asoc.2024.111557 ↗
31. Attribute selection method based on a hybrid BPNN and PSO algorithms10.1016/j.asoc.2012.03.015 ↗
32. BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification10.1016/j.eswa.2021.114842 ↗
33. Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique10.1016/j.jenvman.2018.06.033 ↗
34. Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN10.1016/j.measurement.2016.04.049 ↗
35. When to choose the simple average in forecast combination10.1016/j.jbusres.2016.05.013 ↗
36. An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement10.1016/j.eswa.2022.119421 ↗
37. Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system10.1016/j.ins.2021.10.070 ↗
38. A novel system for multi-step electricity price forecasting for electricity market management10.1016/j.asoc.2019.106029 ↗
39. PSOSVRPos: WiFi indoor positioning using SVR optimized by PSO10.1016/j.eswa.2023.119778 ↗
40. Short-term power load forecasting based on Elman neural network with particle swarm optimization10.1016/j.neucom.2019.02.063 ↗
41. Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm10.1016/j.future.2018.12.068 ↗
42. Optimization of an extreme learning machine model with the sparrow search algorithm to estimate spring maize evapotranspiration with film mulching in the semiarid regions of China10.1016/j.compag.2022.107298 ↗