Garlic price prediction and early warning based on VMD-TCAN model

Garlic is one of the major agricultural products and export commodities in China. It is of great significance for the accurate early warning of garlic price fluctuations in the market. However, the time series of garlic price is often characterized by the nonlinear, non-stationary, and “sharp-peaked and heavy-tailed” distribution. Conventional early warning cannot effectively capture complex temporal features, resulting in limited warning accuracy. In this study, a VMD-TCAN framework was proposed to forecast the garlic price. The early warning model was then graded for the precise prediction and automatic warning. A price alert indicator system was also constructed to divide the warning threshold. Firstly, Variational Mode Decomposition (VMD) was introduced to mitigate the high noise and non-stationarity of the original price series. The optimal solution of variational modes was searched iteratively, and then adaptively decomposed the signal into intrinsic mode functions (IMFs) with specific sparsity and finite bandwidth. Mode mixing and end effects were also reduced in the VMD, unlike Empirical Mode Decomposition (EMD). The Particle Swarm Optimization (PSO) algorithm was employed to reduce the subjectivity in VMD parameter selection. The minimum fuzzy entropy was taken as the fitness function to dynamically determine the optimal number of modes and penalty factors. The garlic price series was thereby decomposed into eight IMFs with the center frequencies. Multi-scale components were effectively separated, such as long-term trends, seasonal fluctuations, and short-term random shocks. Secondly, a TCAN prediction model was developed using multi-source information fusion. Six external influencing factors were selected to cover the production (planting area and planting cost), circulation (inventory and export volume), and market (exchange rate and CPI). A high-dimensional input tensor was then formed, together with the VMD-derived IMF components and daily garlic prices. Causal convolution dilated using the TCAN architecture. The long-range historical dependency features were efficiently extracted to exponentially expand the receptive fields with the temporal causality. A self-attention mechanism was further introduced to globally perceive multi-source inputs. The correlation weights were dynamically assigned to the critical time steps that significantly influenced the price movements. The performance was substantially enhanced to capture the abnormal price volatility intensity and trend turning points under complex market dynamics. Finally, an integrated “forecasting–early warning” framework was established after evaluation. The Shapiro–Wilk test revealed that the garlic price volatility followed the non-normal, sharp-peaked, and heavy-tailed distribution, thereby rendering the conventional rule inapplicable. Therefore, a quantile-based method was adopted in the historical volatility distribution. Five warning levels were defined: severe negative warning, mild negative warning, no warning, mild positive warning, and severe positive warning. Continuous numerical forecasts were thus mapped into discrete risk-level signals. Daily price data were collected from the Jinxiang production area from 2005 to 2024. Experimental results show that the VMD-TCAN model achieved a root mean square error (RMSE) of 0.042 yuan/(500 g) and a coefficient of determination (R²) of 0.998, significantly outperforming benchmark models and their VMD hybrid versions. In terms of early warning performance, the accuracy and F1-score were improved by 4.86 percent points and 4.97 percent points, respectively, compared with the TCN model. More precise identification of abnormal fluctuations was realized in the garlic market. The timely and reliable price forecasts and warnings can greatly contribute to the decision-making in the garlic industry.