ShinglingPFN:A Network Freight Price Prediction Model Based on Local Context Learning
LU Pengfei
ZHANG Ping
WU Jun
WU Xia
LIU Tao
Abstract：To address the problem of decreased transaction rates caused by inaccurate price prediction on online freight platforms,a local context learning model with tabular prior-data fitted network(TabPFN)based on Shingling retrieval(ShinglingPFN)was proposed,which integrated Shingling retrieval and the TabPFN.Firstly,the w-Shingling retrieval algorithm was employed by the model to match the most similar orders to the predicted order from historical order data,and locally associated contextual data was constructed.Secondly,a pre-trained TabPFN model instance was loaded and initialized.The filtered order data were input into the model,and TabPFN was enabled to learn the association patterns between freight features and freight rates based on such contextual information.Finally,the freight rate prediction results of the freight sample were output.The results showed that the mean absolute error(MAE)metric of the ShinglingPFN model was decreased by 30.98%compared with that of the random forest(RF)model.The interpretability of the model was further enhanced through global sensitivity analysis.The ShinglingPFN model could provide decision support for platform optimization of pricing strategies.
Keywords：tabular datadeep learningTabPFNw-Shinglinginformation retrievalnetwork freightprice forecast
Publication Date：2026-03-20
Online Publishing Date：2026-03-26(First online date of this platform, not the publication date of the document)
Pages：8( 41-48 )