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We propose the Taylorformer for time series and other random processes. Itstwo key components are: 1) the LocalTaylor wrapper to learn how and when to useTaylor series-based approximations for predictions, and 2) the MHA-X attentionblock which makes predictions in a way inspired by how Gaussian Processes' meanpredictions are linear smoothings of contextual data. Taylorformer outperformsthe state-of-the-art on several forecasting datasets, including electricity,oil temperatures and exchange rates with at least 14% improvement in MSE on alltasks, and better likelihood on 5/6 classic Neural Process tasks such asmeta-learning 1D functions. Taylorformer combines desirable features from theNeural Process (uncertainty-aware predictions and consistency) and forecasting(predictive accuracy) literature, two previously distinct bodies.

Taylorformer: Probabilistic Predictions for Time Series and other Processes