Time series prediction currently has a wide range of applications in many fields. It can help people make important decisions if they can accurately estimate the future development of events or indicators. However, modeling and accurately predicting time series with different features has become one of the most challenging applications. Therefore, a novel hybrid multi-step prediction model is proposed, called SSA-ConvBiAE. Firstly, the original data is decomposed into different trend components by singular spectrum analysis (SSA). Secondly, we design a new autoencoder network structure based on convolutional long short-term memory (ConvLSTM) and bidirectional gated recurrent unit (BiGRU). Finally, the different components are inputted to the corresponding autoencoders for training and prediction, and the prediction results are fused. To evaluate the predictive performance of our model, we conduct experiments on two real water supply datasets and two publicly available time series datasets. Experimental results show that the proposed model achieves better performance than baseline methods. The source code has been published on https://github.com/VIMLab-hfut/SSA-ConvBiAE.