Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \$50MM.

翻译：货运运输市场价格通常难以准确预测。本文基于签名变换开发了一种新颖的统计技术，构建了用于预测这些市场价格的预测性与自适应模型。该技术基于签名变换的两个关键要素：其一为通用非线性特性，可将特征空间线性化，从而将预测问题转化为线性回归；其二是签名核，可高效计算时间序列数据间的相似性。两者结合，能高效生成特征并精准识别预测过程中的季节性和机制转换。基于该技术的算法已部署于亚马逊卡车运输业务中，即便在新冠疫情和乌克兰冲突期间，其预测准确度仍显著优于商业行业模型，且具备更强的可解释性。此外，该技术能够捕捉商业周期影响及市场异质性，使预测精度提升五倍以上，预估年节约成本达5000万美元。