Multivariate time series prediction is widely applied across various fields. Recently, MLP-based forecasting methods have shown potential due to their efficiency. However, most of these methods analyze complex time series data primarily from a global perspective in the time domain, leading to incomplete feature extraction and affecting prediction accuracy. To address this issue, we propose TF-DPNet, a lightweight Time Frequency Integrated Network with Dual Perspectives for multivariate time series prediction. TF-DPNet leverages both frequency and time domain information, treating the global and local perspectives of time series data as separate inputs to enhance predictive capability. Specifically, TF-DPNet processes these inputs by extending frequency domain information and reconstructing time domain features to capture rich characteristics. Subsequently, a time feature extractor further enhances time domain feature extraction. Finally, the features extracted from dual perspectives are integrated and optimized using a feature amplifier to improve prediction performance. TF-DPNet’s performance is validated on real-world datasets, showing superior forecasting accuracy and computational efficiency.