Abstract: Time series similarity detection plays a critical role in scenarios such as financial data analysis and power data mining. To address the quantization loss issue in existing deep hashing networks for time series, we propose an end-to-end Deep Contrastive Time Series Hash (DCTSH) network. By introducing an adaptive binarization network and hash loss, the method eliminates quantization errors during binary hashing, enabling the model to generate time series hash codes with enhanced expressive effectiveness and generalization capability through end-to-end training. For unlabeled time series data, the negative sample selection in the contrastive learning network is improved via clustering to strengthen time series representation learning. Experimental results on multiple time series datasets demonstrate that DCTSH achieves significantly improved detection accuracy compared to previous methods.