Taylorformer: Probabilistic Modelling for Random Processes including Time Series

• URL: http://arxiv.org/abs/2305.19141v2
• Date: Mon, 23 Sep 2024 15:56:20 GMT
• Title: Taylorformer: Probabilistic Modelling for Random Processes including Time Series
• Authors: Omer Nivron, Raghul Parthipan, Damon J. Wischik,
• Abstract summary: We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations for use in neural network-based probabilistic models, and 2) the MHA-X attention block which makes predictions in a way inspired by how Gaussian Processes' mean predictions are linear smoothings of contextual data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations (used in dynamical systems) for use in neural network-based probabilistic models, and 2) the MHA-X attention block which makes predictions in a way inspired by how Gaussian Processes' mean predictions are linear smoothings of contextual data. Taylorformer outperforms the state-of-the-art in terms of log-likelihood on 5/6 classic Neural Process tasks such as meta-learning 1D functions, and has at least a 14\% MSE improvement on forecasting tasks, including electricity, oil temperatures and exchange rates. Taylorformer approximates a consistent stochastic process and provides uncertainty-aware predictions. Our code is provided in the supplementary material.

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