To overcome limitations in the existing Self-Supervised Learning （SSL） methods for time-series， such as reliance on negative sample pairs and the potential disruption of temporal structure by data augmentation， a Soft Whitening inspired non-contrastive SSL framework for Time-series Forecasting （SWTF） was proposed. In the framework， series representations with dense information and prediction ability were learned without negative samples. Its core method consists of two collaboratively optimized objectives. The first is a Temporal Soft Whitening （TSW） loss， which means that compact representations with rich information were learned by reducing the redundancy between representation dimensions and maintaining the variance of representations， so as to avoid the reliance on negative sample pairs and representation invariance constraints. The second is a denoising reconstruction task， which means that the model was guided to focus on and encode predictive time-series features. To optimize these two objectives jointly， a Siamese encoder was constructed to use parallel multi-scale convolutional networks to capture both micro-level local periodicities and macro-level long-range dependencies efficiently. Experimental results on several benchmark datasets （electricity， weather， and traffic） show that SWTF achieves a Mean Absolute Error （MAE） reduction of approximately 19.6% （13.4% to 26.4%） compared to mainstream baselines such as CoST and PDF（Periodicity Decoupling Framework）， verifying the effectiveness of SWTF in learning robust and highly predictive time-series representations.