Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting
This work addresses uncertainty quantification for multi-step forecasting in domains like resource scheduling and risk management, representing an incremental improvement over existing conformal prediction methods.
The paper tackles the challenge of uncertainty quantification in multi-step time series forecasting by proposing the Dual-Splitting Conformal Prediction (DSCP) method, which outperforms existing variants by up to 23.59% in Winkler Score and reduces carbon emissions by 11.25% in a real-world deployment.
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 the proposed DSCP significantly outperforms existing CP variants in terms of the Winkler Score, achieving a performance improvement of up to 23.59% compared to state-of-the-art methods. Furthermore, we deployed the DSCP approach for renewable energy generation and IT load forecasting in power management of a real-world trajectory-based application, achieving an 11.25% reduction in carbon emissions through predictive optimization of data center operations and controls.