A hybrid TCN-XGBoost model for agricultural product market price forecasting.

ABSTRACT: Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact agricultural production and the broader macroeconomy. Traditional time series models, limited by linear assumptions, often fail to capture the nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN and XGBoost to improve the accuracy of agricultural price volatility predictions. TCN captures both short-term and long-term dependencies using convolutional operations, while XGBoost enhances its ability to model nonlinear relationships. The model uses 65,750 historical data points from rice, wheat, and corn, with a sliding window technique to construct time ser

SUBMITTER: Zhao T 

PROVIDER: S-EPMC12047798 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature