AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction

Abstract

Abstract This paper proposes an attention-based LSTM (AT-LSTM) model for financial time series prediction. We divide the prediction process into two stages. For the first stage, we apply an attention model to assign different weights to the input features of the financial time series at each time step. In the second stage, the attention feature is utilized to effectively select the relevant feature sequences as input to the LSTM neural network for the prediction in the next time frame. Our proposed framework not only solves the long-term dependence problem of time series prediction effectively, but also improves the interpretability of the time series prediction methods based on the neural network. In the end of this paper, we conducted experiments on financial time series prediction task with three real-world data sets. The experimental results show that our framework for time series pre-diction is state-of-the-art against the baselines.