Design of a Weighted Loss Function for Improving Prediction Accuracy of the Dst Index During Geomagnetic Storm Periods

Abstract

I propose a novel loss function, Spike-Aware Weighted MSE (SAW-MSE), which emphasizes prediction accuracy during geomagnetic storm periods by adaptively weighting errors. While traditional LSTM models using mean squared error (MSE) struggle with limitations in capturing extreme Dst variations, our storm-weighted MSE incorporates a dynamic weighting mechanism governed by parameters α and β, where α controls the degree of emphasis on severe geomagnetic storms, and β determines the steepness of the penalty increase in response to more negative Dst value. The experimental result demonstrates that the proposed method improves accuracy during intense storm events, reducing RMSE from 57.88 (LSTM with MSE) to 45.94(LSTM with SAW-MSE). This result suggests that domain-specific loss function, SAW-MSE, can effectively enhance robustness in space weather forecasting.