Hybrid stock market prediction system for predicting stock market trends using machine learning (ML) and deep learning (DL) models, has preprocessing module that converts stock price data into normalized continuous values and binary trends
	VIGNESHWAR K; MAJJI R; MUKILAN P; SRINIVAS D; PALAGATI A
	2025-03-28
专利权人	VIGNESHWAR K (VIGN-Individual) ; MAJJI R (MAJJ-Individual) ; MUKILAN P (MUKI-Individual) ; SRINIVAS D (SRIN-Individual) ; PALAGATI A (PALA-Individual)
申请日期	2025-03-28
专利号	IN202541030315-A
成果简介	NOVELTY - The system has a preprocessing module for converting stock price data into normalized continuous values and binary trends to reduce noise. A machine learning module applies Decision Tree, Random Forest, XGBoost(RTM: Open-source software library), AdaBoost(RTM: Machine learning meta-algorithm), SVC, Naive Bayes, KNN, Logistic Regression, and ANN models for trend prediction. A deep learning module utilizes Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) networks for sequential data analysis. A comparative analysis module evaluates predictive performance based on F1-Score, Accuracy, and ROC-AUC. USE - Hybrid stock market prediction system for predicting stock market trends using machine learning (ML) and deep learning (DL) models. ADVANTAGE - The hybrid stock market prediction system leverages both continuous and binary data to improve predictive accuracy, reduce computational overhead, and enhance the robustness of trend forecasts. The hybrid approach improves predictive accuracy by reducing noise, enhancing pattern recognition, and enabling adaptive trend analysis in dynamic market conditions. The system improves prediction accuracy, reduces noise, and enhances trend forecasting capabilities, making it an invaluable tool for investors, financial institutions, and market analysts. The binary data conversion simplifies trend recognition by converting continuous values into upward and downward trends, thus enhancing model accuracy.
IPC 分类号	G06N-003/02 ; G06N-007/01 ; G06Q-040/04 ; G06Q-040/06
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13393
专题	中国科学院新疆生态与地理研究所
作者单位	1.VIGNESHWAR K (VIGN-Individual) 2.MAJJI R (MAJJ-Individual) 3.MUKILAN P (MUKI-Individual) 4.SRINIVAS D (SRIN-Individual) 5.PALAGATI A (PALA-Individual)
推荐引用方式 GB/T 7714	VIGNESHWAR K,MAJJI R,MUKILAN P,et al. Hybrid stock market prediction system for predicting stock market trends using machine learning (ML) and deep learning (DL) models, has preprocessing module that converts stock price data into normalized continuous values and binary trends. IN202541030315-A[P]. 2025.

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