Cnn lstm stock forecast. py for the XGBoost model.

Cnn lstm stock forecast. From the literature and my experience, I conclude that CNN-LSTM outperforms CNN and LSTM models. CNNs can effectively identify short-term dependencies and relevant features in time series, such as trends or This research aims to address the gap by developing a hybrid model that combines CNNs and LSTMs to enhance the accuracy and robustness of stock trend predictions. In this paper, two models of LSTM and CNN are used to forecast MRF stock price respectively, and the predicted results are analyzed and compared. 03 to 2. 1109/DASA51403. It is hoped that the empirical study of this model in the field of stock price forecasting can broaden the research perspective and enrich the content of stock price forecasting based on neural Sep 8, 2024 · The generated results revealed that CNN performed the worst, LSTM outperformed CNN-LSTM, CNN-RNN outperformed CNN-LSTM, and the suggested single-layer RNN model outperformed all other models. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. 989 for the same dataset. 1155/2020/6622927 A CNN-LSTM Stock Prediction Algorithm A deep learning model for predicting the next three closing prices of a stock, index, currency pair, etc. Dec 8, 2020 · Many papers have been published on CNN, LSTM, and CNN-LSTM for time series. (2020). Oct 22, 2020 · Hindawi Complexity Volume 2020, Article ID 6622927, 10 pages https://doi. Fundamental and technical Abstract: The traditional resolution to forecast stock trends accurately is based on time series models. These examples show that LSTM has huge potential in many complex and multi-factor fields, so the LSTM model was selected to make stock forecasts. Abstract. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. There exist propositions in the literature that have demonstrated that if properly designed and In addition, the impact of different factors on stock prices may be linear or non-linear. org/10. Feb 8, 2025 · Stock price forecasting is a challenging task that has attracted considerable attention in financial research and machine learning applications. Besides, variables such as return, return-sign and open-high are added to the original variables such as opening price, closing price and maximum price. Stocks & ETFs Taking the Shanghai Stock Exchange 50 (SSE 50) stock index as the research object, long short term memory (LSTM)and Convolutional Neural Networks-Long short term memory (CNN-LSTM) helps to established a high accuracy stock index prediction model. This project leverages recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for stock price prediction, showcasing the Abstract This article proposes a novel hybrid network integrating three distinct architectures – CNN, GRU, and LSTM – to predict stock price movements. Within a multifactor analysis, the model takes into account both technical and macroeconomic factors. Nov 22, 2023 · Figure 1: LSTM recurrent neural network structure In stock price forecasting, LSTM can be used to model the time-series relationship of stock prices and thus make predictions of future price movements. Follow our step-by-step tutorial and learn how to make predict the stock market like a pro today! Jan 3, 2024 · These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and Jul 1, 2025 · In this study, we perform a comprehensive comparison of various deep learning approaches, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), hybrid CNN + RNN + Attention architectures, and Transformer models, for stock price prediction system. Although these models demonstrate improved performance over traditional methods, they often overlook the inherent risk associated with financial predictions, which makes them less robust under volatile market conditions. To this end, at first, each day of the time series is classified into TPs and Ordinary Points (OPs); therefore, the investors demand to know the TPs precisely to make fewer trades and gain more profits. LSTM-CNN Hybrid – A modern deep learning model combining Convolutional Neural Networks (CNN) for local pattern extraction and Long Short-Term Memory (LSTM) for long-term dependencies. 25 Sharpe ratio on S&P500 and averaging 1. Using the one parameter at a time method for sensitivity analysis, we also analyze the effect of look back, learning rate, and mini-batch size parameters. Nov 17, 2021 · CNN-LSTM based Models for Multiple Parallel Input and Multi-Step Forecast Different neural network implementations fed with multiple time series for multi-step forecasting horizons Train a CNN to read candlestick graphs, predicting future trend. 9896. By understanding the nuances of LSTM and Nov 1, 2022 · This paper aims to forecast stock price Turning Points (TPs) with a developed hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. Experimental results showed that GA-based CNN-LSTM has higher prediction accuracy than single CNN, LSTM models, and CNN-LSTM model. Feb 14, 2024 · The accuracy of the models was assessed using traditional survey indicators. 2. Jan 1, 2020 · This project demonstrates how to forecast stock prices using: ARIMA – A classic statistical time series model. Feb 19, 2025 · This paper presents a novel hybrid model that integrates long-short-term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. py for the single-layer LSTM, multi-layer LSTM, and bidirectional LSTM models. After optimization and adjustment, the Dec 10, 2024 · Discovery LSTM (Long Short-Term Memory networks in Python. The multi-head attention mechanism and CBAM capture the time correlation in the stock time series, whereas the CNN integrates the characteristics of stock data. Quantitative Stock Forecast with Weather Data Predict stock prices using different CNN-LSTM type networks, refined using weather data of specific regions and companies. Here are two relevant papers on stock price time series forecasting: Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, & Jingyang Wang. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a Secondly, introducing the CNN model to reduce the noise and capture nonlinear structure of the stock data well, CNN-LSTM and CNN-BiLSTM methods can outperform CNN, LSTM, and BiLSTM. 9317207 Recent achievements in speech recognition with noisy input audio data have inspired using the architecture of a combined CNN-LSTM network for stock price forecasting. By constructing a CNN-BiLSTM-Attention composite model, the pa- per achieves predictions for the stock prices of SZ002912, SZ300006, and SS603311 from different sectors. Sep 7, 2022 · Further to this, Kim and Kim introduced a feature fusion LSTM-CNN model to forecast stock prices by using different representations of the same data. 02, the value of RMSE decreases from 5. This study uses CNN and LSTM networks to predict stock prices. Current ticker: AMZN (Amazon Oct 21, 2020 · Stock Price Prediction Using CNN and LSTM- Based Deep Learning Models October 2020 DOI: 10. A CNN-LSTM-Based Model to Forecast Stock Prices. Then, run the neural network or XGBoost models. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Sep 7, 2022 · Request PDF | A Novel LSTM-CNN Architecture to Forecast Stock Prices | With stock market participation increasing worldwide due to a variety of factors such as the prospect of earning dividend Nov 23, 2020 · Stock price data have the characteristics of time series. To improve the accuracy of stock predictions, we construct a model that integrates investor sentiment with Long Short-Term Memory (LSTM) networks. This method uses normalization on whole data instead of window size normalization which paper proposes. Designing robust and accurate predictive models for stock price prediction has been an active area of research over a long time. This paper focuses on addressing the challenges of low prediction accuracy and poor stability, which have been a key area of interest in academic research. Executed a trading strategy based on the predictions of the model, achieving a 1. We used stock data from January 1, 2013 to May 18, 2018, including four characteristic values: the highest price, the lowest price, the opening price and the closing price. This method uses multiple attention mechanisms and a CNN as its main component. LSTMs are a type of recurrent neural network (RNN) that are particularly effective for time series predictions due to their ability to capture long-term dependencies in sequential data. Based previous literature, CNN-LSTM has the potential probabilities to forecast the stock series data, which is worth to research their application on the stock market prediction. Oct 23, 2021 · This finding supports the use of multivariate CNN-LSTM to forecast the value of different stock market indices and that it is a viable choice for research involving the development of models for the study of financial time-series prediction. 0133 shortening of the Z-score range. In this article, we will introduce a new framework based on CNN and LSTM, which aims to aggregate multiple variables (historical data and leading indicators), automati-cally extract features through CNN, and then input them into LSTM to predict the direction of the stock market. This model has only ~700 parameters and consists of convolutions and LSTM layers. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages CNN-LSTM is used to forecast the CSI 300 index to obtain the 10-year data of CSI 300 index. Furthermore, M et al. I Developed a robust CNN model for both classification and regression tasks, leveraging a 2K-day dataset of S&P500 features and 80 other indicators. As financial market data grows and computational advancements continue, traditional linear models fall short of delivering accurate predictions. Mar 7, 2024 · The proposed model was assessed against state-of-the-art DNNs such as CNN, CNN-LSTM, Conv-LSTM, and Stacked LSTM for the adaptive prediction of closing gold price. Our results confirmed that the SARIMA–CNN–LSTM model yields greater forecast accuracy than the individual models. May 30, 2025 · Results indicate that the hybrid CNN-LSTM model outperforms individual LSTM and CNN models, effectively capturing both rapid fluctuations and long-term price trends. e. Oct 26, 2017 · In this paper, the convolutional neural network and long short-term memory (CNN-LSTM) neural network model is proposed to analyse the quantitative strategy in stock markets. With the power of deep learning, we aim to forecast stock prices and make informed investment decisions. txt) or read online for free. Dec 9, 2018 · Therefore, this paper proposes and realizes the CNN and LSTM forecasting model with financial news and historical data of stock market, which uses deep learning methods to quantify text and mine the laws of stock market changes and analyze whether they can predict changes. The workflow starts with an ARIMA baseline and transitions to a hybrid neural network to capture 在参考的这篇论文中，作者使用的特征多达 8 个： eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change。我们这里使用了5个，但是本质原理是一样的。而且在真实建模的时候，数据都被进行了标准化。模型架构 1: CNN + LSTM Sep 14, 2023 · This application has recently received attention, mainly from the financial community, for stock price prediction [6]. Stock price data have the characteristics of time series. Centered on the LSTM algorithm, it employs CNN for feature extraction and introduces BiLSTM and Attention mechanisms to further enhance model ro- bustness. Oct 15, 2024 · By constructing different combination models combining LSTM and CNN, the paper uses normalization to preprocess the split data and realize the generalization prediction of stock price. 05 across major indices including NASDAQ, DJI, NYSE, and RUSSELL. pdf) or read online for free. The CNN - LSTM model is adopted, combining the feature extraction ability of CNN with the long - term dependency Jun 17, 2023 · The Korea Stock Index (KOSPI) data was selected for model evaluation. py for the XGBoost model. CNN-LSTM Model Stock Forecasting Based on an Integrated Attention Mechanism Published in: 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML) LSTM-CNN Stock Price Prediction in PyTorch. S. We employ a suite of predictive models, including Long Nov 23, 2020 · A forecasting method of stock price based on CNN-LSTM which can provide a reliable stock price forecasting with the highest prediction accuracy and provides practical experience for scholars to study financial time series data is proposed. The model is designed to forecast stock prices and trends over a specified period (e. Concurrently, the GNN component leverages Pearson Nov 1, 2024 · The prediction of stock prices is a complex task due to the influence of various factors, high noise, and nonlinearity. Nov 1, 2022 · The models can be used to forecast either the accurate stock rate, induced by the low MSE, RMSE and MAE of LSTM models, or the general trend and deflection range of the stock the following day, induced by the ability to dynamically capture swift changes in the system of CNN models. Aug 28, 2020 · Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. This study combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) for improving the stock prediction accuracy, uses Jan 22, 2023 · Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. Enhancing Stock Market Predictions with a Stacked CNN-LSTM Ensemble Model Abstract: - The prediction of stock market movements remains a complex and challenging task due to the dynamic and intricate nature of financial markets. Sep 6, 2022 · With stock market participation increasing worldwide due to a variety of factors such as the prospect of earning dividend income or poor interest rates being offered by banks, there has been an increased focus by investors to get ahead of the curve by trying to predict the movement of stocks. Secondly, a balancing approach Feb 8, 2025 · The CNN - LSTM model is adopted, combining the feature extraction ability of CNN with the long - term dependency handling ability of LSTM, and the Adam optimizer is used to adjust the parameters. 1007/s00530-021-00758-w License CC BY 4. 9543 to 0. py for our proposed Attention-based CNN-LSTM and XGBoost hybrid model. [12] compared CNN to RNN for the prediction of stock prices of companies in the IT and pharmaceutical sectors. Run Main. Jan 3, 2025 · Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. Apr 12, 2025 · This paper explores utilizing Machine Learning algorithms, specifically LSTM, for stock price prediction. Highly customizable for different stock tickers. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. We also present Due to the nature of stock prices having characteristics of time series data, a range of Deep Learning algorithms can be used to analyze the underlying patterns of stocks. Nov 1, 2021 · The purpose of this study is to verify whether using gold prices, a gold volatility index, crude oil price, and a crude oil price volatility indices as input features can enable a deep learning model accurately to predict future stock price trends, and to discuss separately the applicability of CNN and LSTM models to the abovementioned characteristics and financial indicators. This study helps investors and policy makers who want to use stock price fluctuations as more accurate predictive data using deep learning models. - GitHub - jdank417/Deep-Learning-for-Stock-Market-Predictions: This program provides a comprehensive pipeline for stock price prediction, integrating CNN Jan 14, 2025 · The CNN - LSTM model is adopted, combining the feature extraction ability of CNN with the long - term dependency handling ability of LSTM, and the Adam optimizer is used to adjust the parameters. I have kept Jan 12, 2024 · A Novel Hybrid Approach for Stock Market Index Forecasting using CNN-LSTM Fusion Model January 2024 International Journal of Intelligent Systems and Applications in Engineering 12 (12):266-279 Nov 8, 2020 · In this study, Mehtab and Sen (2020) aimed to make a successful forecast about the future price of the NIFTY 50 index traded on the Indian National Stock Exchange using CNN and LSTM. Dec 1, 2021 · The Convolutional Neural Network (CNN) is used to capture the hierarchical data structure, while the Long Short Term Memory network (LSTM) is used to capture the long-term dependencies in the data. Based on the in-depth study of CNN and LSTM, in order to further improve the stock prediction accuracy, this paper builds a joint stock price prediction model of CNN-LSTM in the PyTorch environment. Nov 27, 2024 · This paper proposes a new method, CNN-CBAM-LSTM. Oct 1, 2024 · In this research, we harness the capabilities of machine learning algorithms to forecast the stock values of the top five enterprises within the Standard & Poor's 500 index, spanning five years. 76 to 2. Utilizing this architecture along with various SMAs and EMAs for denoising, our model evaluates both the future stock price movement and the magnitude of price change. 0. Oct 1, 2023 · It can effectively predict stock market prices by handling data with multiple input and output timesteps. Training & testing Dataset from Huge Stock Market Dataset-Full Historical Daily Price + Volume Data For All U. Experimental results showed that these three methods showed better results compared to similar studies that forecast the direction of price change [20]. Feb 22, 2021 · Based on the operating principles of CNN and LSTM, the input of the initial variable covers the historical data and the leading indicators of the stock, and it can further reach other places of the stock, and then realize the prediction of the data. Mar 20, 2025 · Evaluated on diverse stock datasets from different industries, CLAM demonstrates an average reduction of over 80% in MAE and RMSE compared to standalone CNN, LSTM, and fused CNN-LSTM. py for pre-processing step by ARIMA model. The LSTM component adeptly captures temporal patterns in stock price data, effectively modeling the time series dynamics of financial markets. 1155/2020/6622927 Nov 24, 2022 · ing have shown good results in stock price prediction. here is the paper A CNN-LSTM-Based Model to Forecast Stock Prices. Recent achievements in speech recognition with noisy input audio data have inspired using the architecture of a combined CNN-LSTM network for stock price forecasting. org Welcome to the Stock Market Prediction using LSTM project! This repository contains the code and resources for predicting stock market trends using Long Short-Term Memory (LSTM) neural networks. 0 Nikhil et al. Run XGBoost. edu Feb 5, 2025 · Machine learning, particularly LSTM, has played a significant role in algorithmic trading, accurately forecasting stock prices. It processes time-series data to predict future prices and visualizes the results by comparing predicted trends against actual trends. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to Dec 4, 2024 · Conclusion Stock market forecasting with LSTM is a powerful approach that leverages deep learning to uncover hidden patterns in time series data. In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. Jan 1, 2020 · Stock price data have the characteristics of time series. Jan 3, 2023 · Today, we will use a very simple deep-learning architecture that often gives state-of-the-art results. Nov 24, 2020 · At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. CNN model is a popular and efficient algorithm in the area of finance, which can be combined with many other models to produce more targeted models. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a Jan 3, 2024 · In this regard, the purpose of this study is to compare these two deep learning algorithms, i. Oct 12, 2022 · Combining LSTM and CNN methods and fundamental analysis for stock price trend prediction Published: 12 October 2022 Volume 82, pages 17769–17799, (2023) Cite this article Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. based on the past 10 days of trading history (Open, High, Low, Close, Volume, Day of Week). Using more features makes an improvement to the accuracy as the following pictures show the This program provides a comprehensive pipeline for stock price prediction, integrating CNN for feature extraction and LSTM for sequence modeling, demonstrating a hybrid approach to capture both spatial and temporal patterns in stock data. 2020. . This study aims to construct an effective model to enhance the prediction ability of General Electric's stock price trend. The dynamic nature of the financial markets presents a significant challenge for investors and analysts in making informed decisions. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0. stanford. By combining these two methods, the prediction model can leverage the strengths of CNN and LSTM to improve accuracy and learning performance. , 5 days) using historical stock data. Due to the inherent uncertainty and volatility of the stock market, stock price prediction has always been both intriguing and challenging. Stock price fluctuations are highly complex and non-linear, creating substantial difficulties in making precise predictions within the financial industry. Run LSTM. pdf), Text File (. In recent years, deep learning models have emerged as promising approaches for capturing patterns and dependencies in financial time series data. Metaheuristic algorithms, such as Artificial Rabbits Optimization algorithm (ARO), can be used to optimize the hyperparameters of an LSTM model and improve the accuracy of stock market predictions. Due to the nature of stock prices having characteristics of time series data, a range of Deep Learning A CNN-LSTM-based Model to Forecast Stock Prices - Free download as PDF File (. Implementation of "A CNN-LSTM-Based Model to Forecast Stock Prices" article with pytorch framework - armin-azh/CNN-LSTM A CNN-LSTM-Based Model to Forecast Stock Prices Implemnetation of following paper to predict stock market price via CNN-LSTM. Neural network architecture based on this paper (Lu et al. Nov 24, 2020 · Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. Nov 9, 1985 · Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases. https://doi. The LSTM was used to extract the temporal features of stock data by analyzing the closing prices and trading volume. Dec 28, 2024 · This paper combines the advantages of convolutional neural network (CNN) in feature extraction and the advantages of long short-term memory network (LSTM) in large-time scale relationship Jul 9, 2021 · In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN Feb 24, 2025 · This study explores a hybrid approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, utilizing their strengths to enhance prediction accuracy in various market conditions. This study presents a comparison of two well-known neural network architectures, Convolutional Neural Networks (CNN) and May 21, 2023 · Recently, CNN is now used in Natural Language Processing (NLP) based applications, so by identifying the features from stock data and converting them into tensors, we can obtain the features and then send it to LSTM neural network to find the patterns and thereby predicting the stock market for given period of time. Through Pearson correlation coefficient, SelectKBest and other methods to analyze and screen the characteristics, so as to predict the closing price of Nov 19, 2024 · Cracking the Code: Stock Prediction with Attention-Based LSTM, RNN, and CNN — A Complete Python Guide The stock market, and particularly stock price prediction is an area that draws significant … Apr 2, 2025 · The comparison results show that the CNN-BiLSTM-Attention model has excellent prediction performance, compared with the CNN-LSTM-Attention model, the value of MAE decreases from 5. Here Combining Feature Extraction and Sequence Learning along with Complementary Strengths can improve Predictive Performance. In this paper, two models of LSTM and CNN are used to forecast MRF stock price respectivel , and the predicted results are analyzed and compared. Full explanation is available at [1]. Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting Abstract: Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. This work implements two machine learning models for short-term stock predictions. See full list on cs230. In the data preparation stage, historical trading data of General Electric's stock is collected. Bao et al. A cnn-lstm based model to forecast stock prices - Free download as PDF File (. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. Demonstrates data preprocessing, building an LSTM-CNN architecture, training on Yahoo Finance data, and evaluating future stock price forecasts. This research paper Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. LTSM Stock Predictor Predicting different stock prices using Long Short-Term Memory Recurrent Neural Network in Python using TensorFlow 2 and Keras. Recently, CNN is now used in Natural Language Processing (NLP) based applications, so by identifying the features from stock data and converting them into tensors, we can obtain the features and then send it to LSTM neural network to find the patterns and thereby predicting the stock market for given period of time. View on GitHub LTSM Stock Predictor Predicting different stock prices using Long Short-Term Memory Recurrent Neural Network in Python using TensorFlow 2 and Keras. The paper underscores the significance of LSTM approach in predicting stock values and identifies the most effective models for achieving higher accuracy. How effective is the CNN-LSTM hybrid model in predicting stock trends compared to traditional financial models? Apr 6, 2022 · The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a Nov 1, 2022 · This paper aims to forecast stock price Turning Points (TPs) with a developed hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. We used stock data from January 1, 2013 to May 18, 2018, including four characteristic values: the highest price, the In 2020, Kamalov used MLP, CNN, and LSTM to forecast the stock price of four major US public companies. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time […] Feb 22, 2021 · A graph-based CNN-LSTM stock price prediction algorithm with leading indicators February 2021 Multimedia Systems 29 (3) DOI: 10. Accurate stock price prediction plays a pivotal role in financial markets, influencing investment decisions, risk management, and portfolio optimization. Feb 15, 2019 · Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. May 13, 2025 · We explore the dynamics of the stock market and prominent classical methods and deep learning-based approaches that are used to forecast prices and market trends. May 23, 2025 · The LSTM model outperformed the CNN-LSTM because it is specifically designed to capture temporal dependencies in time series data, which is crucial for stock price forecasting. 74, and the value of R2 increases from 0. [11] used wavelet transforms to remove the noise from stock price series before feeding them to a stack of autoencoders and a long short-term memory (LSTM) NN layer to make one-day price predictions. And then, we adopt LSTM to predict the stock price with the extracted feature data. Recently, deep learning frameworks like LSTM networks and CNNs have become prominent in stock In this study, the historical trading data of General Electric's stock is collected, including information such as opening price, closing price, highest price, lowest price, and trading volume. g. Secondly, a balancing approach The model can capture geographical and temporal patterns in historical stock data by utilizing the potent capabilities of CNN and LSTM architectures, enhancing accuracy and producing trustworthy forecasts. We proposed an optimized ensemble model that combines an LSTM-based attention mechanism and a cyclic multidimensional Mar 17, 2025 · The stock market is a vital component of the financial sector. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of Taking the Shanghai Stock Exchange 50 (SSE 50) stock index as the research object, long short term memory (LSTM)and Convolutional Neural Networks-Long short term memory (CNN-LSTM) helps to established a high accuracy stock index prediction model. To sum up, an amalgamated CNN and LSTM model is employed to forecast stock values within the healthcare industry. Some works use models with neural networks with convolutional layers and also LSTM layers, here called simply CNN-LSTM, in time series applications, as is the case of [11, 18]. , CNN and LSTM, and if CNN provides an accurate result or whether a novel idea such as LSTM gives superior stock prediction outcomes. , 2020). According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. Complexity, 2020. Jan 1, 2025 · Experimental results demonstrate the effectiveness of this method in stock market prediction tasks, showing superior performance compared to individual CNN or LSTM models, as well as traditional statistical methods. Apr 6, 2024 · A multi-level nested ensemble model based on stacking is constructed in this study, which integrates the sentiment-stock Dual-CNN-LSTM model with learners to improve the accuracy of stock price volatility prediction. Jan 1, 2022 · PDF | On Jan 1, 2022, Weidong Xu published Stock Price Prediction based on CNN-LSTM Model in the PyTorch Environment | Find, read and cite all the research you need on ResearchGate Firstly, run ARIMA. Therefore, this paper proposes a combined CNN-LSTM model with good results and generalization ability, which can fully exploit the advantages of each model in stock price prediction and improve the accuracy and stability of prediction. Jan 15, 2025 · Accurate stock price prediction is crucial for investors and financial institutions, yet the complexity of the stock market makes it highly challenging. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. Feb 19, 2025 · Abstract This paper presents a novel hybrid model that integrates long-short-term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. However, traditional time series models simply cannot fit the irregular movements of the market due to their own limitations. The CNN - LSTM model is adopted, leveraging the feature extraction ability of CNN and the ability of LSTM to handle long - term dependencies. This paper proposes a novel LSTM-CNN architecture to predict the closing prices of stocks. Stock market prediction by using CNN-LSTM neural network. [44] propose a Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The CNN extracts short-term features, while the LSTM models long-term dependencies. Methodically, the CNN-LSTM neural network is used to make the quantitative stock selection Jan 1, 2024 · In this research, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) is used to predict gold prices. Secondly, introducing the CNN model to reduce the noise and capture nonlinear structure of the stock data well, CNN-LSTM and CNN-BiLSTM methods can outperform CNN, LSTM, and BiLSTM. Concurrently, the GNN component leverages In this study, the historical trading data of General Electric's stock is collected, including information such as opening price, closing price, highest price, lowest price, and trading volume. The integration of both networks enables a more comprehensive analysis of market trends and patterns, leading to more accurate stock price predictions. Apr 6, 2023 · In this paper, we proposed three RNN-based hybrid models, namely CNN-LSTM, GRU-CNN, and ensemble models, to make one-time-step and multi-time-step predictions of the closing price of three stock market indices in different financial markets. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict Thus, CNN offers the highest level of forecast accuracy, which is the best performing model in contrast to LSTM and Conv1D-LSTM, and is more suitable for investors to predict future stock prices than LSTM and Conv1D-LSTM. Dec 15, 2024 · In this article, we will dive deep into how to build a stock price forecasting model using PyTorch and LSTM (Long Short-Term Memory) networks. This program provides a comprehensive pipeline for stock price prediction, integrating CNN for feature extraction and LSTM for sequence modeling, demonstrating a hybrid approach to capture both spatial and temporal patterns in stock data. pr ph qr ku ts be le xh mo er