Transformer Conformal Prediction for Time Series

Uncertainty quantification has become crucial in many scientific domains where black-box machine learning models are often used [1]. Conformal prediction has emerged as a popular and modern technique for uncertainty quantification by providing valid predictive inference for those black-box models [8, 2]. Time series prediction aims to forecast future values based on a sequence of observations sequentially ordered in time [3]. With recent advances in machine learning, numerous models have been proposed and adopted for various time series prediction tasks. The increased use of black-box machine learning models necessitates uncertainty quantification, particularly in high-stakes time series prediction tasks such as medical event prediction, stock prediction, and weather forecasting. While conformal prediction can provide valid predictive inference for uncertainty quantification, applying conformal prediction to time series is challenging since time series data often violate the exchangeability assumption.