Category-Aware Transformer for Automotive Spare Parts Demand Forecasting

In the modeling process, existing auto parts demand forecasting methods often fail to clearly distinguish structural differences at the demand-frequency level, which results in limited adaptability in real scenarios characterized by sparse demand and a complex category structure. In response to the above problems, this paper proposes a category-aware Transformer framework for auto parts demand forecasting. To explicitly represent demand differences across frequency-based accessory groups and to reduce training bias caused by long-tail sample imbalance, the framework first constructs frequency categories based on statistical demand characteristics and incorporates them into the model as embedding-based structural priors. It then designs a category-perception attention mechanism within the Transformer to guide differentiated modeling of category-related temporal dependencies. At the same time, in order to improve the expression ability of sparse and nonlinear disturbances, this paper introduces a nonlinear mapping module at the prediction end and uses a weighted loss function based on sample distribution to constrain the training target. Experimental results on three real auto-parts demand datasets from Dongfeng Automobile Co., Ltd. show that the proposed method outperforms five benchmark models in terms of RMSE and MAE, with especially strong performance in predicting intermediate-frequency and long-tail parts.