<ul><li>Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends.</li><li>The proposed Dual-Splitting Conformal Prediction (DSCP) method is a novel approach designed to capture inherent dependencies within time-series data for multi-step forecasting.</li><li>Experimental results on real-world datasets demonstrate that DSCP outperforms existing Conformal Prediction methods, achieving a performance improvement of up to 23.59% compared to state-of-the-art techniques.</li><li>DSCP is deployed in a real-world trajectory-based application for renewable energy generation and IT load forecasting, resulting in an 11.25% reduction in carbon emissions through predictive optimization of data center operations and controls.</li></ul>

Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting

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