Transformer Conformal Prediction for Time Series

Junghwan Lee, Chen Xu, Yao Xie

Keywords: Conformal Prediction, Uncertainty Quantification, Machine Learning, Deep Learning, Transformer

TL;DR: Conformal Prediction for Time Series with Transformer

Abstract: We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer decoder as a conditional quantile estimator to predict the quantiles of prediction residuals, which are used to estimate the prediction interval. We hypothesize that the Transformer decoder benefits the estimation of the prediction interval by learning temporal dependencies across past prediction residuals. Our comprehensive experiments using simulated and real data empirically demonstrate the superiority of the proposed method compared to the existing state-of-the-art conformal prediction methods.

Submission Number: 56