Concerning the insufficient integration of local and global dependencies in the existing time series models， a method integrating local and global correlations for multivariate time series prediction， namely PatchLG （Patch-integrated Local-Global correlation method） was proposed. The proposed method was based on three key components： 1） segmenting the time series into multiple patches， thereby preserving the locality of the time series while making it easier for the model to capture global dependencies； 2） utilizing the depthwise separable convolution and self-attention mechanism to model local and global correlations； 3） decomposing the time series into trend and seasonal items to perform predictions simultaneously， and combining the prediction results of these two items to obtain the final result. Experimental results on seven benchmark datasets demonstrate that PatchLG achieves average improvements of 3.0% and 2.9% in Mean-Square Error （MSE） and Mean Absolute Error （MAE）， respectively， compared to the optimal baseline method PatchTST （Patch Time Series Transformer）， and has low actual running time and memory usage， validating the effectiveness of PatchLG in time series prediction.