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：货运运输市场费率通常难以准确预测。本研究开发了一种基于签名变换的新型统计技术，并构建了一个预测性与自适应模型来预测这些市场费率。我们的技术基于签名变换的两个关键特性：一是其通用非线性特性，它能够线性化特征空间，从而将预测问题转化为线性回归；二是签名核，它允许高效计算时间序列数据之间的相似性。两者结合，能够在预测过程中实现高效特征生成，并精确识别季节性和状态转换。基于我们技术的算法已在亚马逊卡车运输业务中部署，与市售行业模型相比，即使在COVID-19大流行和乌克兰冲突期间，也展现出远优于行业模型的预测精度和更好的可解释性。此外，我们的技术能够捕捉商业周期的影响和市场异质性，将预测准确度提高五倍以上，预计每年可节约5000万美元。