Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
This work addresses forecasting challenges in retail pricing, offering incremental improvements through novel tokenization methods.
The authors tackled time series forecasting by proposing a transformer architecture with multiple-resolution tokenization, applied to a real-world pricing problem at a large retailer, where their model outperformed in-house and existing deep learning models.
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.