PT-LSTM: Extending LSTM for Efficient Processing Time Attributes in Time Series Prediction

Long Short-Term Memory (LSTM) has been widely applied in time series predictions. Time attributes are important factors in time series prediction. However, existing studies often ignore the influence of time attributes when splitting the time series data, and seldom utilize the time information in the LSTM models. In this paper, we propose a novel method named Position encoding and Time gate LSTM (PT-LSTM). We first propose a position-encoding based time attributes integration method, which obtains the vector representation of time attributes through position encoding, and integrate it with the observed value vectors of the data. Moreover, we propose a LSTM variant by adding a new time gate which is specially designed to process time attributes. Therefore, PT-LSTM can make good use of time attributes in the key phases of data prediction. Experimental results on three public datasets show that our PT-LSTM model outperforms the state-of-the-art methods in time series prediction.