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24 articles for “Stock Prediction”
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Research ArticleOpen accessIn the abstractPredictive Analytics and Procurement Visibility: A Big-Data-Driven Approach to Customer Satisfaction in Supply Chain ManagementDevendra Nath Pathak, Rakesh Kumar Yadav, Hitendra SinghIn the current dynamic and consumer driven market environment, supply chains need to move out of reactive to the proactive ecosystems. One of the most urgent requirements is increasing procurement visibility and the development of better order planning for the inventory to meet the products’ demand with continued high customer satisfaction. This paper is an agenda of big-data-driven predictive analytics … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 2,442 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting of Multistep Multivariate Financial Data Through GSO Algorithm Infused Vanilla LSTM ModelNikhitha Pai, Ilango Velchamy, Nithya B RameshPredicting time series in the financial domain requires using models that can do sequence processing tasks. One such model is the LSTM model which belongs to the family of deep learning networks. The performance measure of LSTM in a regression task is to bring down the error in the predicted values. In this study, a novel fused LSTM model integrating … …Journal of Computer Science, Vol. 19, Issue 7 · 10 Jul 2023 · 6,074 accessesView PDFCite
Research ArticleOpen accessIn the abstractKalman Filtering for Stocks Price Prediction and ControlJimbo Henri Claver, Mbiazi Dave, Shu Felix CheStocks price analysis has been a critical area of research as the stock market is a very fluctuating market. Stocks price is affected by demand and supply dynamics making it difficult to forecast the price of a stock at a particular instant. The entire idea of predicting stocks price is to gain significant profits but predicting how the stock market … …Journal of Computer Science, Vol. 19, Issue 6 · 20 May 2023 · 6,367 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,967 accessesView PDFCite
Literature ReviewOpen accessIn the abstractOverview and Future Opportunities of Sentiment Analysis Approaches for Big DataNurfadhlina Mohd Sharef, Harnani Mat Zin, Samaneh NadaliThe ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common … …Journal of Computer Science, Vol. 12, Issue 3 · 26 Apr 2016 · 10,313 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractFUZZY MODEL OPTIMIZATION FOR TIME SERIES DATA USING A TRANSLATION IN THE EXTENT OF MEAN ERRORNurhayadi, Subanar, Abdurakhman, Agus Maman AbadiRecently, many researchers in the field of writing about the prediction of stock price forecasting, electricity load demand and academic enrollment using fuzzy methods. However, in general, modeling does not consider the model position to actual data yet where it means that error is not been handled optimally. The error that is not managed well can reduce the accuracy of … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 2 Jun 2014 · 6,733 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting the Spanish Stock Market Returns with Fractional and Non-Fractional ModelsGuglielmo Maria Caporale, Juncal Cunado, Luis A. Gil-AlanaProblem statement: The content of this note was to assess the forecasting accuracy of various models of the Spanish stock market returns. Approach: We use daily data on the IBEX 35 for the time period January 4th, 2001-March 28th, 2006 and employ both fractional and non-fractional models. Results: The results on the prediction errors for the out-of-sample forecasts indicate that … …American Journal of Economics and Business Administration, Vol. 3, Issue 4 · 8 Dec 2011 · 7,316 accessesView PDFCite
Research ArticleOpen accessIn the abstractDesign and Implementation of Novel Artificial Neural Network Based Stock Market Forecasting System on Field-Programmable Gate ArraysMd. Syedul Amin, Md. Mamun, Fazida Hanim Hashim et al.Problem statement: Multiagent system is very proficient and has rules well-suited for financial forecast with its neural network. In financial forecasting, the approach for rules extractions is less pertinent and involves algorithms which are complex. The unsupervised network method lacks in comprehensibility and leads to ambiguity. Approach: The application of neural network technology to real-time processing of financial market analysis … …American Journal of Applied Sciences, Vol. 8, Issue 10 · 12 Sep 2011 · 7,627 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Artificial Immune System with Artificial Neural Network for Predicting Bombay Stock Exchange TrendsM. Gunasekaran, K. S. RamaswamiProblem statement: The purpose of this study is to develop an artificial immune system for recognizing stock market trends and predict upward and downward directions of stock market. This study compared two prediction models, an Artificial Immune System (AIS) and Artificial Neural Network (ANN) for predicting the future index value, trend of Indian stock market and discovers the best prediction … …Journal of Computer Science, Vol. 7, Issue 7 · 28 Jun 2011 · 7,075 accessesView PDFCite