Dual-splitting conformal prediction for multi-step time series forecasting

Abstract

Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends. Uncertainty Quantification (UQ) is a mainstream approach for addressing forecasting uncertainties, with Conformal Prediction (CP) gaining attention due to its model-agnostic nature and statistical guarantees. However, most variants of CP are designed for single-step predictions and face challenges in multi-step scenarios, such as reliance on real-time data and limited scalability. This highlights the need for CP methods specifically tailored to multi-step forecasting. We propose the Dual-Splitting Conformal Prediction (DSCP) method, a novel CP approach designed to capture inherent dependencies within time-series data for multi-step forecasting. Experimental results on real-world datasets from four different domains demonstrate that DSCP significantly outperforms existing CP variants in terms of the Winkler Score, improving performance by up to 23.59% compared to state-of-the-art methods. Furthermore, the deployment of DSCP for renewable energy generation and IT load forecasting in the power management of a real-world trajectory-based application achieves an 11.25% reduction in carbon emissions through predictive optimization of data center operations and control strategies.