Financial series prediction using Attention LSTM

by   Sangyeon Kim, et al.

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.


page 1

page 2

page 3

page 4


EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction

Time series prediction with deep learning methods, especially long short...

Asset Pricing and Deep Learning

Traditional machine learning methods have been widely studied in financi...

Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots

Cross-correlation analysis is a powerful tool for understanding the mutu...

An alarm prediction framework for financial IT system using hybrid machine learning methods

Informatization grows rapidly in all walks of life, going with the enhan...

On the balance between the training time and interpretability of neural ODE for time series modelling

Most machine learning methods are used as a black box for modelling. We ...

Process Outcome Prediction: CNN vs. LSTM (with Attention)

The early outcome prediction of ongoing or completed processes confers c...

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Risk prediction, as a typical time series modeling problem, is usually a...

Please sign up or login with your details

Forgot password? Click here to reset