Share Market Prediction Using Long Short Term Memory and Artificial Neural Network

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.

Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity.

Investors, researchers and finance practitioners are continuously looking for the best technique that can assist them in accurately predicting the stock markets. The ability to predict stock prices contradicts the efficient market hypothesis (EMH) and can yield substantial monetary rewards for investors. Various stock price prediction techniques are used to predict the stock market and they range from statistical to machine learning methods. Statistical models fall short in handling nonlinear data which characterizes most stock markets. Artificial Neural Networks (ANNs), one of the widely used techniques are able to handle nonlinear data but have low prediction accuracy due to their inability to handle long term dependencies and memory capacity handling. Prediction models that have an ability to learn long-term dependency information are ideal for stock market prediction. The current study uses deep learning techniques, namely, Long Short Term Memory (LSTM), Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), Bidirectional LSTM (BLSTM), Bidirectional RNN (BRNN), Bidirectional GRU (BGRU) to predict stock markets in ten sub-Saharan African countries. The prediction techniques were run on a python 3.5 environment using Theano and Keras libraries. Limited computing capacity was of great concern. However, for the purpose of this study, access to high performance computing facilities was granted in order to run the experiments. Experimental results show that both unidirectional and bidirectional architectures greatly improved prediction accuracy in this research. However, both architectures were found not to be significantly different in predicting the stock markets of the ten African countries. In general, LSTMs followed by BGRUs proved to be the best models in predicting the African stock markets.

The estimation of possible fluctuations in stock prices has been the focus of a lot of research work. Price prediction is a technique for predicting a stock's potential future price, and as a result, the price. This study shows how we can use Machine Learning Models based on Long Short-Term Memory (LSTM) to forecast the price of a stock. Stock prices may be anticipated with a high degree of accuracy if correctly modeled, according to certain suggestions. There is also a lot of literature on basic analysis of stock prices, which focuses on detecting and learning from trends in stock price movements. The focus of this research is on stock market forecasting utilizing Long Short-Term Memory (LSTM) models. For the purpose of our study, we have used DSE30's top 10 companies' historical data. We have built two LSTM models to predict and compare the results of the prediction. To train these models, we used training data that consisted of these companies' stock records from January, 2019 till January, 2021. Our target was to find out which version of the LSTM architecture model gives the best prediction among these models.

Understanding the pathogenesis of epilepsy including changes in synaptic pathways can improve our knowledge about epilepsy and development of new treatments. In this regard, data-driven models such as artificial neural networks, which are able to capture the effects of synaptic plasticity, can play an important role. This paper proposes long short term memory (LSTM) as the ideal architecture for modeling plasticity changes, and validates this proposal via experimental data. As a special class of recurrent neural networks (RNNs), LSTM is able to track information through time and control its flow via several gating mechanisms, which allow for maintaining the relevant and forgetting the irrelevant information. In our experiments, potentiation and depotentiation of motor circuit and perforant pathway as two forms of plasticity were respectively induced by kindled and kindled + transcranial magnetic stimulation of animal groups. In kindling, both procedure duration and gradual synaptic changes play critical roles. The stimulation of both groups continued for six days. Both after-discharge (AD) and seizure behavior as two biologically measurable effects of plasticity were recorded immediately post each stimulation. Three classes of artificial neural networks-LSTM, RNN, and feedforward neural network (FFNN)-were trained to predict AD and seizure behavior as indicators of plasticity during these six days. Results obtained from the collected data confirm the superiority of LSTM. For seizure behavior, the prediction accuracies achieved by these three models were 0.91 ± 0.01, 0.77 ± 0.02, and 0.59 ± 0.02%, respectively, and for AD, the prediction accuracies were 0.82 ± 0.01, 0.74 ± 0.08 and 0.42 ± 0.1, respectively.

COVID-19 special issue: Intelligent solutions for computer communication-assisted infectious disease diagnosis.

Predicting the price of stocks has always been a chief vicinity of studies for plenty of years. Accurate predictions can assist buyers to make accurate choices for the selling and buying of stocks. In order to make accurate choices for selling and buying of stocks, precise predictions play a vital role for the buyers. The ambitions of the paper are to estimate and gauge inventory expenses and patterns by making proper utilization of the machine learning, for providing investors a primary device for eager hypotheses in the particular risky Indian Stock Market. In this paper, to examine and expect the inventory charge with the assistance of sentiment analysis and machine learning algorithms is been used. The main algorithm used is long short-term memory (LSTM) which has the benefits of reading relationships amongst time collection facts via its reminiscence function. A divine technique of inventory charge primarily based on the algorithm mentioned above his been advised. Use of Linear Regression, Decision Tree, Recurrent Neural Network, Support Vector Machine, Radial Basis Function, Long short-term memory (LSTM) and different forecasting models is been discussed in this research. Results has been observed before finalizing the long short-term memory (LSTM) algorithm to analyze the stock price one by one and the outcomes of these models are compared and studied. In this research, the dataset used contains the costs of the stocks from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> January 1962 to 15 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> October 2021. Based on this dataset, eight features- consisting of commencing charge, maximum charge, lowest charge, final charge, change of the stocks, turnover, ups and downs, and volume have been considered. After using several machine learning and neural network algorithms, it can be concluded that the Long Short-Term Memory (LSTM) model provides reliable stock price forecasting with the best prediction accuracy. This forecasting approach presents a brand new study concept for inventory rate forecasting and also provides better practical experience for scholars to study financial time series data.

Stock prices are co-integrated with fundamental valuation factors (earnings, revenue, cash flow, etc.). This has allowed legendary value investors such as Warren Buffett to enjoy tremendous investment success over the long-term. However, the market is efficient at discounting past information and consensus future expectations. Having the ability to read non-obvious information and to have a better prediction at future valuation factors (surprises from consensus estimates) would likely lead to investment outperformance (generating alpha returns) over the long run. In a recently published work, Alberg and Lipton used various artificial neural networks (ANN) to predict companies’ valuation ratios from their historical fundamental factors and suggested promising results. Aligned with this thought, this research project aims at using Long Short-Term Memory (LSTM) neural network, a type of recurrent neural network (RNN), to predict future fundamental valuation factors of companies and then test investment results by applying active risk-return optimized portfolio strategies. The reasons for choosing LSTM network for this study are the following: (1) like a deep neural network, LSTM is a flexible universal function approximator suited for time-series forecast; (2) LSTM, unlike vanilla version of RNN, does not suffer from “vanishing gradient problem” and is well suited in discovering long-range characteristics, hence its name. Given that LSTM network (or deep neural network in general) enjoys a reputation of being prone to over-fitting to in-sample data, we spend a significant amount of efforts in studying the over-fitting behavior and try to lay out systematic procedures in detecting and mitigating such issues. The current work gives us confidence and excitement that much more can be explored to potentially further improve the prediction performance and investment returns.

-- The stock market is inherently volatile and influenced by a wide array of factors including economic indicators, political events, market sentiment, and company-specific news. Accurately predicting stock prices has long been a challenge for investors, traders, and researchers alike. This project, titled "Stock Market Prediction", aims to leverage advanced machine learning techniques to forecast future stock prices based on historical data and key market indicators. The study employs a combination of supervised learning algorithms such as Linear Regression, Support Vector Machines (SVM), and ensemble methods like Random Forest, as well as deep learning models including Long Short-Term Memory (LSTM) neural networks. These models are trained on historical stock price data, technical indicators (such as moving averages, RSI, MACD), and, optionally, sentiment analysis from financial news and social media. Feature selection and data preprocessing techniques such as normalization, data smoothing, and time-series windowing are applied to enhance model accuracy and stability. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score are used to assess model performance. Among the approaches tested, LSTM models demonstrate superior accuracy in capturing temporal dependencies and nonlinear trends in stock price movements. The system also includes a user-friendly interface that allows users to input stock ticker symbols and receive predictive insights and visualizations of expected price trends. This project not only highlights the potential of AI in financial forecasting but also underscores the limitations posed by market unpredictability, overfitting risks, and external variables that cannot be quantified easily. The findings contribute to the growing field of algorithmic trading and financial analytics, offering a practical tool for decision-makers and investors. Index Terms—Stock Market Prediction, Machine Learning, Deep Learning, LSTM, Time Series Forecasting, Financial Analytics, Price Prediction

Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able to determine stock market projections. Predictions are used to reduce risk when making transactions. In this study, predictions of stock price trends were made using the Recurrent Neural Network (RNN). The approach taken is to perform a time series analysis using the RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in the LSTM model testing simulation can estimate stock prices with maximum percentage accuracy. The results showed that the prediction model produced a loss function of 0.0012 and a training time of 73 m/step. The evaluation was carried out with the RMSE which resulted in a score of 17.13325. Predictions are obtained after doing machine learning using 1239 data. The RMSE and LSTM models are calculated by changing the number of epochs, the variation between the predicted stock price and the current stock price. Computations are carried out using a stock market dataset that includes open, high, low, close, adj prices, closes, and volumes. The main objective of this study is to determine the extent to which the LSTM algorithm anticipates stock market prices with better accuracy. Code can be seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Stock Prediction, Time Series, Recurrent Neural Network (RNN), Long Short Term Memory (LSTM).

Neural network based approaches have recently produced record-setting performances in natural language understanding tasks such as word labeling. In the word labeling task, a tagger is used to assign a label to each word in an input sequence. Specifically, simple recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have shown to significantly outperform the previous state-of-the-art - conditional random fields (CRFs). This paper investigates using long short-term memory (LSTM) neural networks, which contain input, output and forgetting gates and are more advanced than simple RNN, for the word labeling task. To explicitly model output-label dependence, we propose a regression model on top of the LSTM un-normalized scores. We also propose to apply deep LSTM to the task. We investigated the relative importance of each gate in the LSTM by setting other gates to a constant and only learning particular gates. Experiments on the ATIS dataset validated the effectiveness of the proposed models.

Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network

Recognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various Indian scripts, only Bangla has this additional difficulty of tackling mixed cur-siveness of its handwriting style in the pipeline of a method towards its automatic recognition. A few other common recognition difficulties of handwriting in an Indian script include the large size of its alphabet and the extremely cursive nature of the shapes of its alphabetic characters. These are among the reasons of achieving only limited success in the study of unconstrained handwritten Bangla text recognition. Artificial Neural Network (ANN) models have often been used for solving difficult real-life pattern recognition problems. Recurrent Neural Network models (RNN) have been studied in the literature for modeling sequence data. In this study, we consider Long Short Term Memory (LSTM) network model, a useful member of this family. In fact, Bidirectional Long Short-Term Memory (BLSTM) neural networks is a special kind of RNN and have recently attracted special attention in solving sequence labelling problems. In this article, we present a BLSTM architecture based approach for unconstrained online handwritten Bangla text recognition.

Thermally-induced error compensation of spindle system based on long short term memory neural networks