1. Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
- Author
-
Peršak, Egon, Anjos, Miguel F., Lautz, Sebastian, and Kolev, Aleksandar
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
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.
- Published
- 2024