Research on NBA Sport Card Price Prediction Technology Based on the Transformer Architecture

As NBA player cards become an important investment asset, accurately predicting their price trends becomes especially critical for investors. Traditional price forecasting methods often fail to capture complex market dynamics and long-term dependencies effectively. Therefore, this study proposes a price prediction model for NBA player cards based on Transformer architecture to improve the accuracy and reliability of price prediction. We crawled NBA Top Shot data using Node.js to obtain historical data for multiple player cards from March 11, 2021 to March 21, 2021. Multimodal data is integrated, including historical price data, player performance data and card physical characteristics. The data is cleaned and normalized, and sequences are constructed using sliding window techniques to meet the input requirements of the model. Regularization techniques and appropriate early stopping strategies are employed to prevent overfitting. The model design consists of encoder and decoder components, which capture long-term dependencies in time series through self-attention mechanism and make predictions of future prices. Experimental results show that the proposed model is significantly superior to the benchmark model in terms of mean square error (MSE) and mean absolute error (MAE) on the test set. In addition, we compare the Transformer model with LSTM and GRU models in terms of computational efficiency and prediction accuracy. These findings provide valuable decision support tools for investors in the NBA player card market and demonstrate the potential of Transformer-based models in processing complex time series data, providing important references for future research and practical applications.