DPAST-RNN: A Dual-Phase Attention-Based Recurrent Neural Network Using Spatiotemporal LSTMs for Time Series Prediction

For time series forecasting, the weight distribution among multivariables and the long-short-term time dependence are always very important and challenging. Traditional machine forecasting can’t automatically select the effective features of multivariable input and can’t capture the time dependence of sequences. The key to solve this problem is to capture the spatial correlations at the same time, the spatiotemporal relationships at different times and the long-term dependence of the temporal relationships between different series. In this paper, inspired by human attention mechanism including encoder-decoder model, we propose DPAST-based RNN (DPAST-RNN) for long-term time series prediction. Specifically, in the first phase we use attention mechanism to extract relevant features at each time adaptively then we use stacked LSTM units to extract hidden information of time series both from time and space dimensions. In the second phase, we use another attention mechanism to select the related hidden state in encoder to the hidden state of the decoder at the current time to make context vector which is embed into recurrent neural network in decoder. Thorough empirical studies based upon the VM-Power dataset we collected on OpenStack and the NASDAQ 100 Stock dataset demonstrate that the DPAST-RNN can outperform state-of-the-art methods for time series prediction.