In multivariate time series forecasting (MTSF), traditional channel dependence models typically adopt the “fusion first, individuality last” mechanism, where variables are initially fused before learning their temporal patterns. However, this mechanism often obscures the unique characteristics of individual variables due to the strong coupling effect during early fusion, ultimately compromising prediction accuracy. To address this limitation, this paper proposes a novel MTSF framework called Individuality Prioritization and Fusion Postponement (IPFP), which fundamentally reverses the conventional paradigm. IPFP first makes independent predictions for each variable based on its own temporal patterns. Then, it captures the historical correlations among multiple variables from historical time series and predicts their future correlations. Finally, it integrates the individual predictions by weighting them according to the forecasted correlations. In addition, in order to predict the correlations among variables in the future, inspired by multistep prediction in time series forecasting, this paper introduces a way to predict the correlations among variables at the future multiple time steps based on their historical correlation sequence. Experimental results on seven datasets show that IPFP outperforms the existing mainstream models in accuracy.