Aiming to detect time series anomalies, deep learning methods generally use the recurrent neural network or long short term memory to capture temporal dependency, and adopt autoencoder to reconstruct data. Although they work well for detecting anomalies, the network structures of these methods are complex, resulting in slow computational efficiency. In order to improve the computational efficiency, this paper proposes a method called serial autoencoders based anomaly detection (SAE-AD) which contains two autoencoders (AE_1$ and AE_2$) with simple structure. Due to the simplicity, there are a few training parameters and its training objectiv function is relatively simple, which speeds up the computation. In addition, the output of AE_1$ is fed into AE_2$ to improve the decoding ability of the decoder of AE_2$. This way of serial training makes SAE-AD achieve better detection accuracy. Experiment results show that the proposed method has better precision, recall, $F_1$ score than several state-of-art anomaly detection methods.