# Financial series prediction using Attention LSTM

arXiv: https://arxiv.org/abs/1902.10877 · HTML: https://mlsanity.com/paper/1902.10877/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation](https://mlsanity.com/paper/2105.00250/) (2021)
- [Rethinking Attention Mechanism in Time Series Classification](https://mlsanity.com/paper/2207.07564/) (2022)
- [Transformers versus LSTMs for electronic trading](https://mlsanity.com/paper/2309.11400/) (2023)
- [Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment](https://mlsanity.com/paper/2205.01639/) (2022)
- [Modelling Concurrency Bugs Using Machine Learning](https://mlsanity.com/paper/2305.05531/) (2023)
- [Transforming Credit Risk Analysis: A Time-Series-Driven ResE-BiLSTM Framework for Post-Loan Default Detection](https://mlsanity.com/paper/2508.00415/) (2025)
- [L2GTX: From Local to Global Time Series Explanations](https://mlsanity.com/paper/2603.13065/) (2026)

## Abstract (arXiv)

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.