  Financial time series prediction, especially with machine learning
techniques, is an extensive field of study. In recent times, deep learning
methods (especially time series analysis) have performed outstandingly for
various industrial problems, with better prediction than machine learning
methods. Moreover, many researchers have used deep learning methods to predict
financial time series with various models in recent years. In this paper, we
will compare various deep learning models, such as multilayer perceptron (MLP),
one-dimensional convolutional neural networks (1D CNN), stacked long short-term
memory (stacked LSTM), attention networks, and weighted attention networks for
financial time series prediction. In particular, attention LSTM is not only
used for prediction, but also for visualizing intermediate outputs to analyze
the reason of prediction; therefore, we will show an example for understanding
the model prediction intuitively with attention vectors. In addition, we focus
on time and factors, which lead to an easy understanding of why certain trends
are predicted when accessing a given time series table. We also modify the loss
functions of the attention models with weighted categorical cross entropy; our
proposed model produces a 0.76 hit ratio, which is superior to those of other
methods for predicting the trends of the KOSPI 200.
