Accurate forecasting of potato prices is essential for agricultural market stability and policy decision-making. However, price series are typically nonlinear, non-stationary, and exhibit sharp temporal fluctuations that hinder reliable modeling. This study proposes a Zeta-Alignment Loss (ZA Loss), a frequency-temporal alignment objective that enforces consistency between the spectral magnitude and phase of predicted and true sequences. Unlike traditional regression losses that minimize pointwise errors, ZA Loss constrains models to maintain coherence across both temporal and frequency domains, reducing phase lag and over-smoothing effects. Experiments on a high-resolution multi-provincial potato price dataset demonstrate that integrating ZA Loss into various neural architectures consistently improves forecasting accuracy and stability. The results highlight that frequency-temporal alignment serves as an effective principle for producing dynamically consistent and interpretable forecasts under complex agricultural market conditions.
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