Keywords: [ Theory ] [ Deep Learning ]
Current approaches for multi-horizon time series forecasting using recurrent neural networks (RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical application domains where an uncertainty estimate is also required. Existing approaches for uncertainty quantification in RNN-based time-series forecasts are limited as they may require significant alterations to the underlying model architecture, may be computationally complex, may be difficult to calibrate, may incur high sample complexity, and may not provide theoretical guarantees on frequentist coverage. In this paper, we extend the inductive conformal prediction framework to the time-series forecasting setup, and propose a lightweight algorithm to address all of the above limitations, providing uncertainty estimates with theoretical guarantees for any multi-horizon forecast predictor and any dataset with minimal exchangeability assumptions. We demonstrate the effectiveness of our approach by comparing it with existing benchmarks on a variety of synthetic and real-world datasets.