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109 results for “Time Series Analysis”
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Research ArticleOpen accessIn the keywordsStock Closing Price Forecasting Using LSTM, Sentiment Analysis, Kalman FilterAssel Abdildayeva, Galymzhan Nurtugan, Guldana Taganova, Ardak AkhmetovaThe article explores the problem of predicting the closing prices of stocks using an innovative approach that combines methods of machine learning, sentiment analysis and noise filtering. The focus is on the development and testing of a comprehensive model that integrates recurrent neural networks (LSTM) for time series analysis, the GPT-3 language model for processing and analyzing textual data from … …Journal of Computer Science, Vol. 20, Issue 11 · 4 Sep 2024 · 6,262 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModel Comparison for the Prediction of Stock Prices in the NYSEVictoria Switlyk, Junfeng ShangThe stock market is an integral part of investments as well as the economy. The prediction of stock prices is an exciting and challenging problem that has been considered by many due to the complexity and noise within the market and to the potential profit that can be yielded from accurate predictions. We aim to construct and compare models used … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 26 Sep 2019 · 7,475 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAdvanced Multivariate Time Series Forecasting ModelsMiss Lei WangIn this study, the focus is to collect and summarize various advanced forecasting models for multivariate time series dataset. We have discussed about the inherent forecasting strengths and weaknesses related to these time series modelings. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R softwares improve the … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 21 Dec 2018 · 7,316 accessesView PDFCite
Case ReportOpen accessIn the keywordsEvaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow ForecastingKassahun Birhanu Tadesse, Megersa Olumana Dinka, Tena Alamirew, Semu Ayalew MogesReservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for … …American Journal of Environmental Sciences, Vol. 13, Issue 5 · 28 Sep 2017 · 7,885 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA BIO-SURVEILLANCE SYSTEM USING EFFICIENT ALGORITHM FOR INFORMATION SHARING APPLICATIONSShobhanjaly P. Nair, K. Silambarasan, N. Bindhu, R. SrinivasanBio-Surveillance represents a health information system for public that stores and integrates health related information and data across a variety of data. Bio-surveillance strategies are projected for detection of disease outbreaks from existing diagnostic data, but their performance is typically not understandable. The focus of the paper is to develop a bio-surveillance system which can serve the aim of biosurveillance … …Journal of Computer Science, Vol. 10, Issue 7 · 17 Jun 2014 · 7,038 accessesView PDFCite
Research ArticleOpen accessIn the keywordsSeasonal Autoregressive Integrated Moving Average Model for Precipitation Time SeriesYan Wang, Xinghua Chang, Meng Gao, Xiyong HouPredicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 20 Feb 2013 · 12,655 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical DatabasesR. Sethukkarasi, A. KannanThe analysis and representation of temporal data are becoming increasingly important in many areas of research and application. The existing Fuzzy Cognitive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to represent a complex causal system as a collection of concepts and causal relationships among concepts. … …Journal of Computer Science, Vol. 8, Issue 11 · 7 Nov 2012 · 6,358 accessesView PDFCite
Research ArticleOpen accessIn the keywordsResemblance of Rain Fall in Bangladesh with Correlation Dimension and Neural Network LearningAbu Nasir Mohammad Enamul Kabir, Hussain Muhammad Imran Hasan, Mohd Abdur Rashid et al.Rain fall and Temperature are undoubtedly two important factors that balance water in the environment. Adequate study of the rain behavior helps to forecast it. The time series obtained from different stations of the country throughout the several years are collected and analyzed. The dynamics of rain fall time series is analyzed with Correlation Dimension (CD) to characterize the several … …American Journal of Applied Sciences, Vol. 10, Issue 10 · 5 Sep 2013 · 7,234 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTime Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive ModelSuhartonoProblem statement: Most of Seasonal Autoregressive Integrated Moving Average (SARIMA) models that used for forecasting seasonal time series are multiplicative SARIMA models. These models assume that there is a significant parameter as a result of multiplication between nonseasonal and seasonal parameters without testing by certain statistical test. Moreover, most popular statistical software such as MINITAB and SPSS only has facility … …Journal of Mathematics and Statistics, Vol. 7, Issue 1 · 29 Jan 2011 · 18,295 accessesView PDFCite
Research ArticleOpen accessIn the keywordsInverse Correlations for Multiple Time Series and Gaussian Random Fields and Measures of Their Linear DeterminismR.J. Bhansali, L. IppolitiFor a discrete-time vector linear stationary process, {X(t)}, admitting forward and backward autoregressive representations, the variance matrix of an optimal linear interpolator of X(t), based on a knowledge of {X(t-j), j≠0}, is known to be given by Ri(0)-1 where Ri(0) denotes the inverse variance of the process. Let A=Is-1Ri(0)ˉ1R(0)ˉ1, where R(0) denotes the variance matrix of {X(t)} and Is an … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,424 accessesView PDFCite