Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing

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.

@article{peršak2025_2407.03185,
  title={ Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing },
  author={ Egon Peršak and Miguel F. Anjos and Sebastian Lautz and Aleksandar Kolev },
  journal={arXiv preprint arXiv:2407.03185},
  year={ 2025 }
}