Abstract: As an important device for transmitting electric energy in the power system, the oil filled terminal of high-voltage cable can reliably predict the concentration of dissolved gas of silicone oil filled in the oil filled terminal, which can provide some supports for the fault diagnosis of silicone oil. Therefore, this paper proposes a prediction model of dissolved gas concentration in silicone oil based on local outlier factor and ICEEMDAN-IPSO-ELM. Firstly, an experimental platform is built to simulate the aging of silicone oil inside the cable terminal, and the concentration sequence of dissolved gas in silicone oil is obtained through chromatographic analysis. Furthermore, the data of the concentration time series of dissolved gas in silicone oil are cleaned, the local outlier detection method is used to judge the abnormal value and make reasonable correction, and then the improved adaptive noise complete set empirical mode decomposition is used to decompose the corrected concentration sequence of dissolved gas in silicone oil. The eigenmode function components with different time scales can effectively reduce the interaction between high and low frequency components. Secondly, aimed at the frequency components with different characteristics, an extreme learning machine network prediction model is built. Aimed at the problem of difficult selection of extreme learning machine model parameters, an improved particle swarm optimization algorithm is used to optimize the weight and threshold parameters of the model, which optimizes the optimization ability of particle swarm optimization method to a certain extent and improves the reliability of combined prediction method. Finally, the predicted content of dissolved gas concentration in silicone oil can be obtained by adding the calculation results of different frequency components. Specific examples show that, compared with other prediction models, this method can be adopted to reliably predict the future trend of dissolved gas content in silicone oil, and provides a strong guarantee for silicone oil fault diagnosis technology.