We propose a transformer architecture for time series forecasting with a focus on time series tokenisation and apply it to a real-world prediction problem from the pricing domain. Our architecture aims to learn effective representations at many scales across all available data simultaneously. The model contains a number of novel modules: a differentiated form of time series patching which employs multiple resolutions, a multiple-resolution module for time-varying known variables, a mixer-based module for capturing cross-series information, and a novel output head with favourable scaling to account for the increased number of tokens. We present an application of this model to a real world prediction problem faced by the markdown team at a very large retailer. On the experiments conducted our model outperforms in-house models and the selected existing deep learning architectures.

我们提出了一种用于时间序列预测的Transformer架构，重点在于时间序列的分词，并将其应用于来自定价领域的真实预测问题。我们的架构旨在同时学习所有可用数据的多个尺度上的有效表示。模型包含几个创新模块：采用多个分辨率的时间序列分割、用于处理时间变化的已知变量的多分辨率模块、用于捕捉交叉序列信息的混合器模块，以及一种对输出进行优化的新颖输出头，以适应增加的令牌数量。我们将该模型应用于一家非常大零售商的降价团队所面临的真实预测问题中。在所进行的实验中，我们的模型优于内部模型和选择的现有深度学习架构。

时间序列预测的多分辨率标记化及其在定价中的应用