Abnormal patterns detection in data (abnormal detection) is a very important research direction in the field of data analysis, and especially the anomaly detection of time series is one of the difficulties. At present, there are many researches on anomaly detection of time series data. Different techniques such as sliding window, wavelet analysis, Probabilistic Graphical Model (PGM), and Recurrent Neural Network (RNN) are used to detect the abnormal patterns. However, these techniques still have some deficiencies in processing this problem, which cannot guarantee real-time efficiency and accuracy in real engineering. In this paper, an anomaly detection algorithm based on Attention-GRU and iForest is proposed for periodic time series. The model is constructed based on Attention-GRU to predict long data sequence and ensure the detection efficiency in engineering. iForest is used to establish a normal data fluctuation range, avoiding the error caused by hypothesis testing. Practice verification shows that this model can improve the anomaly detection efficiency of periodic time series data in actual engineering, and has better recall rate and accuracy.