A study of European wheat price series forecasting based on multiple variants of Holt-Winters and LSTM models

Abstract

Given the importance of wheat in global food security and agricultural economy, accurate price forecasts are of significant practical significance for growers, the food chain, and government macro-control. Four different time series forecasting models were used in this study, including the traditional Holt-Winters three-parameter exponential smoothing method and three improved models based on the Long Short-Term Memory Network (LSTM): the CNN-LSTM, the GRU, and the VMD-LSTM. By comparing the forecasting effects of these models under the same training and test sets, this study aims to select the best models using the best model for predicting European wheat prices from 2010 to 2023 after training. The results show that the CNN-LSTM model performs the best in predicting the European wheat price series, and the future wheat price will show a slow downward trend, and the CNN-LSTM model combines the feature extraction ability of convolutional neural network and the long-term dependency learning ability of LSTM, which can effectively capture the fluctuation characteristics of the price series and the trend characteristics. The GRU model also shows a better prediction ability, especially in dealing with long-term dependencies. in dealing with long-term dependencies. However, the VMD-LSTM model has certain shortcomings in its prediction effect when facing time series data with large fluctuations in the later period. In addition, the Holt-Winters model predicts that after a period of slow decline, wheat prices may rebound, which may be related to the adjustment of market supply and demand and changes in external factors.