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Spectral temporal graph neural network for multivariate agricultural price forecasting
- Source :
- Ciência Rural, Vol 54, Iss 1 (2023)
- Publication Year :
- 2023
- Publisher :
- Universidade Federal de Santa Maria, 2023.
-
Abstract
- ABSTRACT: Multivariate time series forecasting has an important role in many real-world domains. Especially, price prediction has always been on the focus of researchers. Yet, it is a challenging task that requires the capturing of intra-series and inter-series correlations. Most of the models in literature focus only on the correlation in temporal domain. In this paper, we have curated a new dataset from the official website of Turkish Ministry of Commerce. The dataset consists of daily prices and trade volume of vegetables and covers 1791 days between January 1, 2018 and November 26, 2022. A Spectral Temporal Graph Neural Network (StemGNN) is employed on the curated dataset and the results are given in comparison to Convolutional neural networks (CNN), Long short-term memory (LSTM) and Random Forest models. GNN architecture achieved a state-of-the-art result such as mean absolute error (MAE): 1,37 and root mean squared error (RMSE): 1.94). To our knowledge, this is one of the few studies that investigates GNN for time series analysis and the first study in architecture field.
- Subjects :
- Temporal GNN
time series
CNN
LSTM
forecasts
Agriculture
Agriculture (General)
S1-972
Subjects
Details
- Language :
- English, Portuguese
- ISSN :
- 16784596 and 01038478
- Volume :
- 54
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Ciência Rural
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6d4294fd1b44800afa064351fca4089
- Document Type :
- article
- Full Text :
- https://doi.org/10.1590/0103-8478cr20220677