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

• URL: http://arxiv.org/abs/2407.03185v1
• Date: Wed, 3 Jul 2024 15:07:16 GMT
• Title: Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing
• Authors: Egon Peršak, Miguel F. Anjos, Sebastian Lautz, Aleksandar Kolev,
• Abstract summary: We propose a transformer architecture for time series forecasting with a focus on time series tokenisation. Our architecture aims to learn effective representations at many scales across all available data simultaneously. We present an application of this model to a real world prediction problem faced by the markdown team at a very large retailer.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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