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.

翻译：我们提出了一种用于时间序列预测的Transformer架构，重点关注时间序列标记化，并将其应用于定价领域的实际预测问题。我们的架构旨在同时学习所有可用数据在多个尺度上的有效表示。该模型包含多个新颖模块：采用多分辨率的时间序列分块差分形式、用于时变已知变量的多分辨率模块、基于混合器的跨序列信息捕获模块，以及具有良好扩展性的新型输出头以应对标记数量的增加。我们将该模型应用于某超大型零售商降价团队面临的实际预测问题。在进行的实验中，我们的模型优于内部模型及选定的现有深度学习架构。