Attention LSTM for Time Series Forecasting of Financial Time Series Data

Time series Forecasting has attracted attention over the last decade with the boost in processing power, the amount of data available and the development of more advanced algorithms. It is now widely used in a range of different fields including Medical Diagnostics, Weather Forecasting, Financial time series etc. In this paper, we propose a model of attention mechanism that allows for attended input to be fed to the model instead of the actual input. The motivation for the model is to show a new way to view the input so that the model can make more accurate predictions. The proposed LSTM model with the attention mechanism is then evaluated on common evaluation metrics and the results are compared with state of art models like CNN-LSTM and Stacked LSTM to show its benefits.