ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning

Abstract

Ocean temperature data anomaly detection is instrumental in monitoring environmental changes and implementing measures to alleviate adverse consequences. This holds immense importance for marine environmental observation and scientific inquiry. However, existing anomaly detection methods encounter significant challenges in extracting features from data, which severely affects the performance of anomaly detection. Existing models have limitations in capturing the local contextual distribution features and high stochastic distribution trends of ocean temperature data. Therefore, this paper introduces the ConvTrans-CL model, which integrates the Transformer encoder with causal convolution and employs a contrastive learning approach. Causal convolution extracts the distributional features of short-time subsequences within a sliding window and integrates them into the self-attention mechanism, enabling the model to focus on the localized features of the data. Contrastive learning efficiently captures the long-range dependencies of time-series data by distinguishing between pairs of subsequences with adjacent time intervals and pairs of non-adjacent subsequences. This enables the model to capture the trend of high stochasticity in the distribution of the temperature data. Finally, we select temperature data from two sea areas that are susceptible to multiple environmental factors for experiments and compare the feature extraction and anomaly detection capabilities of ConvTrans-CL with other methods, confirming the superior performance of our method.