A Temporal Bias Correction using a Machine Learning Attention model

Climate models are biased with respect to real world observations and usually need to be calibrated prior to impact studies. The suite of statistical methods that enable such calibrations is called bias correction (BC). However, current BC methods struggle to adjust for temporal biases, because they disregard the dependence between consecutive time-points. As a result, climate statistics with long-range temporal properties, such as heatwave duration and frequency, cannot be corrected accurately, making it more difficult to produce reliable impact studies on such climate statistics. In this paper, we offer a novel BC methodology to correct for temporal biases. This is made possible by i) re-thinking BC as a probability model rather than an algorithmic procedure, and ii) adapting state-of-the-art machine-learning (ML) probabilistic attention models to fit the BC task. With a case study of heatwave duration statistics in Abuja, Nigeria, and Tokyo, Japan, we show striking results compared to current climate model outputs and alternative BC methods.

Taylorformer: Probabilistic Predictions for Time Series and other Processes

We propose the Taylorformer for time series and other random processes. Its two key components are: 1) the LocalTaylor wrapper to learn how and when to use Taylor series-based approximations for predictions, 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 on several forecasting datasets, including electricity, oil temperatures and exchange rates with at least 14% improvement in MSE on all tasks, and better likelihood on 5/6 classic Neural Process tasks such as meta-learning 1D functions. Taylorformer combines desirable features from the Neural Process (uncertainty-aware predictions and consistency) and forecasting (predictive accuracy) literature, two previously distinct bodies.