To solve the problem that power transformer vibration signals is difficult to predict because of the non-stationary characteristic, an autoregressive integrated moving average prediction model based on improved dung beetle optimizer algorithm is proposed. Firstly, ADF test and KPSS test are used to check the stationary of the transformer original vibration signal, and if it is not stationary, differential processing is performed until the signal is stationary. Secondly, the periodic mutation mechanism is introduced into dung beetle optimizer algorithm to improve the optimization ability of the algorithm, and the parameters p and q of autoregressive integrated moving average model are determined by improved dung beetle optimizer algorithm to realize the prediction of transformer vibration signal. Finally, the validity of the proposed model is verified by using the actual collected vibration data of a 0. 4- / 0. 4-kV, 15-kVA three-phase doublewinding dry-type transformer. The simulation result shows that the mean absolute percentage error of the model can reach 3. 77%, while the mean absolute percentage error of the autoregressive integrated moving average model, long short-term memory network, recurrent neural network and convolutional neural network are 5. 34%, 4. 74%, 5. 03% and 5. 40%, respectively. Therefore, the proposed model can achieve accurate prediction of transformer vibration signal.