RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect.

ABSTRACT: In this study, we investigate appropriate machine learning methods for predicting agricultural commodity prices. Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time series dataset combining wholesale prices of four agricultural commodities in South Korea, six weather variables, and week numbers. We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. Also, we utilized a univariate prediction model as a control to evaluate the effectiveness of the multivariate

SUBMITTER: Min Y 

PROVIDER: S-EPMC12012190 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature