Title: Towards Unsupervised Time Series Representation Learning: A Decomposition Perspective. Abstract: Existing contrastive methods of universal time series representation learning mainly rely on distilling invariant patterns at varying scales and building contrastive loss with the help of negative sampling. However, the invariance assumptions may not hold in real-world time-series data, and the infamous negative sampling could bring in new biases for representation learning. In this work, we propose a novel contrastive learning approach toward time series representation learning on top of trend-seasonality decomposition, namely TS-DC. TS-DC differentiates itself from prior methods in three folds: 1) a time series decomposition approach is devised to distill different aspects/components of a complex time series; 2) a novel component-wise contrastive loss is proposed in which negative sampling is not necessary; 3) the informative signals of time series can be captured comprehensively by means of adaptive contrasting. Extensive experiments on different public benchmark datasets validate the superior performance of our proposed representation learning method. 