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43 articles for “stock modelling”
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Stock Price Analysis and Forecasting Using Linear Regression and SVM Classifiers
Abstract: In this study, we attempt to implement a Machine Learning approach to predict stock market prices. Linear Regression is very effectively implemented in forecasting stock prices, returns, and stock modelling. This project is for common users as the prediction is done on all of the companies. We outline the design of the Linear Regression model with its salient features and customizable parameters. We select a certain group of parameters with …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 62–66 Read article
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Artificial Neural Network Model for Stock Market Forecasting
Abstract: AbstractIn recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks or stock markets. Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. The aim of this paper is to predict stock market using artificial neural networks (ANNs). The authors used feed forward neural network trained by back-propagation algorithm to make …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 1, 2014 · pp. 7–12 Read article
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Time Series Model for National Stock Price Prediction
Abstract: The national stock exchange is widest and fully automatic trading system in India. Analysis and prediction of stock market time series data have involved considerable interest from the researchers over the last decade. In this paper, the Nifty 50 closing stock market prices were computed and predicted the trend of stock market fluctuations using time series modeling techniques, like exponential smoothing and autoregressive integrated moving average. The forecasted values of …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 85s–90s Read article
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Comparative Performance Study: Deterministic vs. Probabilistic Models in Retail Chains
Abstract: The finished goods, raw materials, and product stock that a business has on hand for sale are referred to as inventory. They enable the companies to achieve their sales levels and are a chance to cost control and decision making. It is a huge asset to a manufacturing firm. Inventory model permits forecasting of quantities of raw material, inventory and spare parts of the equipment to a very high level …
Published in Recent Trends in Mathematics · Vol. 2, Issue 1, 2025 · pp. 1–6 Read article
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Stock Market Forecasting Using Time Invariant Fuzzy Time Series Model
Abstract: Stock market volatility is an important for investment, option pricing and financial market regulation. In recent years a different types of models which apparently predict changes in stock market prices have been introduced. There are many methods in the literature to solve the problem of future prediction. The present study provides a foundation for the development and application of fuzzy time series model for short term investor as well as …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 104s–111s Read article
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Econometric Modeling for Oil Sectors Stock Prices Time Series Data
Abstract: This study examines the dynamic relationships of stock oil prices of different Indian oil sectors viz., Bharat Petroleum, Hindustan Oil Corporation and Oil and Natural Gas Corporation based on the different econometric models. Empirical result shows that the monthly oil prices are non-stationary and integrated of order one. Johanson procedure to test for the possibility of co-integration relationships result shows that there is no co-integration relationship. Since the variables are …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 2, 2018 · pp. 27–34 Read article
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Cointegration and Error Correaction Modeling for BSE and NSE Stock Prices Time Series Data
Abstract: AbstractEconometrician always have been observed that most of the economics time series are non-stationary. The ordinary least square technique could be applied to estimate the model parameters only if the variables have been found to be stationary, i.e., they do not have unit roots. Otherwise, an alternative approach has to be followed, which is ‘co-integration’. Co-integration is a method of finding out the long-term relationship between economic variables under consideration. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 4, Issue 3, 2017 · pp. 30–41 Read article
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Stock Price Movement Prediction using Machine Learning Algorithms and Time Series Models: A Review
Abstract: The stock market is a very important place for investment in a country. It also serves as the index of the growth for an economy. To determine the behavior of the stock market trends has been the focal point of researchers for very long. The nonlinear structure of the stock market makes it challenging to predict how the stock price will change. But it has been demonstrated that stock market …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 2, 2022 · pp. 7–13 Read article
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A Linear Regression Model Used to Analysis the Tesla Stock Price Prediction Using Machine Learning
Abstract: The stock market is a fascinating sector of the economic research. It comes in a number of varieties. Several specialists have been examining and investigating the several patterns that the stock market experiences fluctuations. Predicting the stock values of different companies using historical data has been one of the primary research projects. Stock price prediction can help people a great deal by helping them understand where and how to invest, …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 2, 2024 · pp. 8–13 Read article
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Analysis of Gold Price Trend Using the Hidden Markov Model
Abstract: This study aims to analyze the behavior of gold prices in India through a two-state Hidden Markov Model (HMM). We first formulated crucial parameters, such as the Transition Probability Matrix (TPM), Initial Probability Vector (IPV), and Emission Probability Matrix (EPM). Subsequently, we constructed a hidden Markov probability distribution and evaluated Pearson’s coefficients to gauge the correlations separately for each state. The goodness of fit of the developed model was assessed …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 11, Issue 2, 2024 · pp. 7–16 Read article
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Predicting And Forecasting Stocks
Abstract: Stock value estimation may be a well-liked and vital topic in money and tutorial studies. Share Market is associate untidy place for predicting since there aren't any vital rules to estimate or predict the value of a share within the share market. Several ways like technical analysis, basic analysis, statistical analysis, and applied mathematics analysis, etc. area unit all want to conceive to predict the value within the share market …
Published in Journal of Electronic Design Technology · Vol. 13, Issue 1, 2022 · pp. 1–5 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
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A study to assess the knowledge and attitude towards healthy and regular breakfast among undergraduate students of Moradabad, U.P.
Abstract: A research titled “A study to assess knowledge and attitude towards healthy and regular breakfast among undergraduate students of selected colleges of Moradabad, UP, was conducted in partial fulfillment of the requirement of a degree of ‘Bachelors of Science in Nursing at Vivekanand College of Nursing, Moradabad, UP. The present study is an attempt to determine the students’ knowledge towards healthy and regular breakfast, which in turn will help the …
Published in Journal of Nursing Science & Practice · Vol. 11, Issue 1, 2021 · pp. 6–22 Read article
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Stock Market Prediction with LSTM-based Neural Networks: An Empirical Analysis
Abstract: A stock market is a composite of markets and exchanges where the buying and selling of publicly traded company stocks occur on a regular basis. It serves as a marketplace for the stocks of corporations that are available to the public. Companies opt for an initial public offering (IPO) on the primary market as a means to generate capital. People buy stocks primarily in the hope that they may rise …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 1, 2023 · pp. 9–14 Read article
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Visualizing And Forecasting Stocks Using Single Page Application
Abstract: In the fields of finance and economics, stock price forecasting is a vital and crucial subject. The stock market is not governed by any significant rules that may be used to anticipate or estimate the price of a stock. In an effort to forecast the price in the stock market, several techniques are employed, including technical analysis, fundamental analysis, time series analysis, statistical analysis, etc. However, none of these techniques …
Published in Journal of Electronic Design Technology · Vol. 13, Issue 2, 2022 · pp. 23–28 Read article
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A Review on Various Approaches of Intrusion Detection System and Random Forest in Data Mining
Abstract: With the development in Information and Communication Technology (ICT), it have become a fundamental factor of human’s life. But this technology has brought lots of threats in cyber world. These threats increase the chances of network vulnerabilities to attack the structure in the network. DM based intrusion area techniques consistently fall into any of the two classes; anomaly detection and mistreat finding. In general course of DM alludes to extricating …
Published in Recent Trends in Programming languages · Vol. 5, Issue 1, 2018 · pp. 6–13 Read article
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Stock Price Prediction Using Data Science Techniques
Abstract: This study centers around persuasively presenting the potential to forecast the stability of future market stocks. Previous research has delved into predicting the trajectory of future market trends, leading to fluctuations in stock data, which opens avenues for refinement. The proposed model employs data science methodologies to predict the stock price index's value. This is achieved by contrasting supervised classification data science learning algorithms that predict either stock price increases …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 33–41 Read article
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Evaluation and Scientific Investigation: Stock Market Forecasting Techniques
Abstract: Analysts and scholars have consistently shown interest in predicting stock market trends, a complex task given the multitude of variables influencing stock values. This article includes a thorough analysis of 50 research papers that propose methodology for stock market prediction, including Bayesian models, fuzzy classifiers, artificial neural networks (ANNs), support vector machines (SVMs) classifiers, neural networks (NNs), and machine learning techniques. The collected papers are categorized using various prediction, clustering …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 26–40 Read article
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Bayesian Inference to Time Series Data Mining
Abstract: The time series data mining (TSDM) framework is a fundamental contribution to the field of time series analysis and data mining in the recent past. Methods based on the TSDM framework are able to successfully characterize and predict complex, nonperiodic, irregular and chaotic time series. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis techniques by adopting data mining concepts for analyzing time series. …
Published in Journal of Advanced Database Management & Systems · Vol. 1, Issue 3, 2014 · pp. 10–14 Read article