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

Xuan Zhang · Xun Liang · Aakas Zhiyuli · Shusen Zhang

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

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.

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References

1997Intraday periodicity and volatility persistence in financial markets 2001GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics 1977Long-term dependence in common stock returns 2005AUTOMATED INFERENCE AND LEARNING IN MODELING FINANCIAL VOLATILITY 2008Using neural networks and data mining techniques for the financial distress prediction model

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