CoRe: Coherence and Relational Alignment for Multivariate Time Series Forecasting
Read the original on arXiv Machine Learning →CoRe is a model‑agnostic learning objective for direct multivariate time‑series forecasting that replaces pointwise errors with two output‑space constraints: a frequency coherence loss aligning predicted and target spectra, and a low‑rank relational graph loss matching pairwise differences in a PCA subspace. The objective introduces no trainable parameters and can be applied to existing forecasting backbones by changing only the loss. Experiments on standard benchmarks show that CoRe improves strong baselines, compares favorably with recent forecasting objectives, and remains effective across different backbones, datasets, and hyperparameter settings.
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