scholarly journals The Application of Stock Index Price Prediction with Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 53
Author(s):  
Penglei Gao ◽  
Rui Zhang ◽  
Xi Yang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Financial Markets ◽  
Short Term Memory ◽  
Historical Data ◽  
Stock Index ◽  
Main Task ◽  
Short Term ◽  
Price Prediction ◽  
Long Short Term Memory

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.

scholarly journals Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

2020 ◽  
Vol 27 (3) ◽  
pp. 373-389 ◽  
Author(s):  
Ashesh Chattopadhyay ◽  
Pedram Hassanzadeh ◽  
Devika Subramanian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Data Driven ◽  
Reservoir Computing ◽  
Short Term ◽  
Term Memory ◽  
Machine Learning Methods ◽  
Long Short Term Memory ◽  
Lorenz 96

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.

scholarly journals Investigation of decision making support in digital trading

2020 ◽  
Author(s):  
Ilona Stalovinaitė ◽  
Nijolė Maknickienė ◽  
Raimonda Martinkutė-Kaulienė
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Financial Markets ◽  
Short Term Memory ◽  
Short Term ◽  
Learning Technique ◽  
Technical Indicator ◽  
Long Short Term Memory ◽  
Investment Portfolios ◽  
Decision Making Support

In order to trade successfully investors are looking for the best method to determine possible directions of the price changes of financial means. The main objective of this paper is to evaluate the results of digital trading using different decision-making techniques. The paper examines deep learning technique known as Long Short – Term Memory (LSTM) neural network and parabolic stop and reverse (SAR) technical indicator as possible means for decision-making support. Based on an investigation of theoretical and practical aspects of digital trading and its support possibilities, investment portfolios in real-time “IQ Option” digital trading platform were created. Short-term results show that investment portfolios created using LSTM neural network performed better compared to the ones that were created using technical analysis. The study contributes to the development of new decision-making algorithms that can be used for forecasting of the results in the financial markets.

scholarly journals A Multivariate Long Short-Term Memory Neural Network for Coalbed Methane Production Forecasting

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2045
Author(s):  
Xijie Xu ◽  
Xiaoping Rui ◽  
Yonglei Fan ◽  
Tian Yu ◽  
Yiwen Ju
Keyword(s):  
Neural Network ◽  
Coalbed Methane ◽  
Short Term Memory ◽  
Historical Data ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Long Short Term Memory ◽  
Cbm Production ◽  
Coalbed Methane Production

Owing to the importance of coalbed methane (CBM) as a source of energy, it is necessary to predict its future production. However, the production process of CBM is the result of the interaction of many factors, making it difficult to perform accurate simulations through mathematical models. We must therefore rely on the historical data of CBM production to understand its inherent features and predict its future performance. The objective of this paper is to establish a deep learning prediction method for coalbed methane production without considering complex geological factors. In this paper, we propose a multivariate long short-term memory neural network (M-LSTM NN) model to predict CBM production. We tested the performance of this model using the production data of CBM wells in the Panhe Demonstration Area in the Qinshui Basin of China. The production of different CBM wells has similar characteristics in time. We can use the symmetric similarity of the data to transfer the model to the production forecasting of different CBM wells. Our results demonstrate that the M-LSTM NN model, utilizing the historical yield data of CBM as well as other auxiliary information such as casing pressures, water production levels, and bottom hole temperatures (including the highest and lowest temperatures), can predict CBM production successfully while obtaining a mean absolute percentage error (MAPE) of 0.91%. This is an improvement when compared with the traditional LSTM NN model, which has an MAPE of 1.14%. In addition to this, we conducted multi-step predictions at a daily and monthly scale and obtained similar results. It should be noted that with an increase in time lag, the prediction performance became less accurate. At the daily level, the MAPE value increased from 0.24% to 2.09% over 10 successive days. The predictions on the monthly scale also saw an increase in the MAPE value from 2.68% to 5.95% over three months. This tendency suggests that long-term forecasts are more difficult than short-term ones, and more historical data are required to produce more accurate results.

scholarly journals Comparação entre LSTM e CLCNN na detecção de requisições maliciosas em ataques na web

2021 ◽  
Author(s):  
Rafael Bosse Brinhosa ◽  
Marcos A. Michels Schlickmann ◽  
Eduardo da Silva ◽  
Carlos Becker Westphall ◽  
Carla Merkle Westphall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Cross Site

Com o uso de aplicações web em ambientes dinâmicos de computação em nuvem integrados com dispositivos IoT, os ataques de injeção de SQL e de XSS (Cross-Site Scripting) continuam causando problemas para a segurança. A detecção de requisições maliciosas a nível de aplicação representa um desafio na pesquisa, que está evoluindo usando técnicas de Machine Learning e redes neurais. Este trabalho apresenta a comparação entre duas arquiteturas de aprendizado de máquina usadas para detectar requisições web maliciosas: LSTM (Long Short-Term Memory) e CLCNN (Character-level Convolutional Neural Network). Os resultados demonstram que a CLCNN é a mais eficaz em todas as métricas, com uma acurácia de 98,13%, precisão de 99,84%, taxa de detecção em 95,66% e com um F1-score de 97,70%.

scholarly journals A Recurrent Neural Network-Based Method for Dynamic Load Identification of Beam Structures

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7846
Author(s):  
Hongji Yang ◽  
Jinhui Jiang ◽  
Guoping Chen ◽  
M Shadi Mohamed ◽  
Fan Lu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dynamic Load ◽  
Short Term Memory ◽  
Load Identification ◽  
Short Term ◽  
Structural Dynamic ◽  
Term Memory ◽  
Structural Dynamic Characteristics ◽  
Long Short Term Memory

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

scholarly journals An improved long short-term memory neural network for stock forecast

2018 ◽  
Vol 232 ◽  
pp. 01024
Author(s):  
Liujia Lv ◽  
Weijian Kong ◽  
Jie Qi ◽  
Jue Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Error ◽  
Short Term Memory ◽  
Historical Data ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory ◽  
Closing Price ◽  
Long Short Term Memory

This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.

scholarly journals Predicting LQ45 financial sector indices using RNN-LSTM

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Seng Hansun ◽  
Julio Christian Young
Keyword(s):  
Financial Markets ◽  
Network Architecture ◽  
Stock Price ◽  
Short Term Memory ◽  
Short Term ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Short Period ◽  
Long Short Term Memory ◽  
Lstm Network

AbstractAs one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period of time and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the long short-term memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. Rather than using too deep network architecture, we propose using a simple three-layer LSTM network architecture to predict the stocks’ closing prices. We found that the prediction results fall in the reasonable forecasting category. Moreover, it is worth noting that two of the considered stocks, namely, BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135, which fall in the good forecasting results. Hence, the proposed LSTM model is most recommended to be used on those two stocks.

Sign in / Sign up

Export Citation Format

Share Document