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

TL;DR

This paper introduces a multi-resolution transformer architecture for time series forecasting, specifically applied to pricing data, demonstrating superior performance over existing models in a real-world retail scenario.

Contribution

The paper presents a novel multi-resolution transformer model with unique modules for time series tokenization and cross-series information capture, tailored for pricing prediction tasks.

Findings

Model outperforms in-house and existing deep learning models

Effective multi-scale representations improve forecasting accuracy

Application demonstrates practical utility in retail pricing

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.