Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
<|reference_start|>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.<|reference_end|>
@article{peršak2024multiple-resolution,
    title={Multiple-Resolution Tokenization for Time Series Forecasting with an
  Application to Pricing},
    author={Egon Perv{s}ak, Miguel F. Anjos, Sebastian Lautz, Aleksandar Kolev},
    journal={arXiv preprint arXiv:2407.03185},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03185},
    primaryClass={cs.LG cs.AI}
}