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Financial time series prediction, especially with machine learningtechniques, is an extensive field of study. In recent times, deep learningmethods (especially time series analysis) have performed outstandingly forvarious industrial problems, with better prediction than machine learningmethods. Moreover, many researchers have used deep learning methods to predictfinancial time series with various models in recent years. In this paper, wewill compare various deep learning models, such as multilayer perceptron (MLP),one-dimensional convolutional neural networks (1D CNN), stacked long short-termmemory (stacked LSTM), attention networks, and weighted attention networks forfinancial time series prediction. In particular, attention LSTM is not onlyused for prediction, but also for visualizing intermediate outputs to analyzethe reason of prediction; therefore, we will show an example for understandingthe model prediction intuitively with attention vectors. In addition, we focuson time and factors, which lead to an easy understanding of why certain trendsare predicted when accessing a given time series table. We also modify the lossfunctions of the attention models with weighted categorical cross entropy; ourproposed model produces a 0.76 hit ratio, which is superior to those of othermethods for predicting the trends of the KOSPI 200.

Financial series prediction using Attention LSTM