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262 results for “Time Series”
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Research ArticleOpen accessIn the keywordsFuzzy Logic-Based Truck Demand Computational ModelAnugrah Mauludin, Ditdit Nugeraha UtamaTrucking is one of the essential aspects linking suppliers and customers in many countries. Services at a trucking service provider will be based on a first-come-first-served basis. When a customer contacts a trucking service provider, there is a possibility that all the truck units owned are in fact being used to serve other customers causing potential delays in their delivery … …Journal of Computer Science, Vol. 19, Issue 9 · 19 Aug 2023 · 5,474 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDiscrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG RecordingsHayat Sedrati, Wajih Rhalem et al.Alcoholism is a serious public health issue, and early diagnosis of this brain disease can be performed by analyzing Electroencephalogram (EEG) signals. However, the high dimensionality of EEG datasets requires significant computational time and resources for the automatic processing of EEG signals. This study proposes a novel method to reduce EEG dataset dimensionality using a Discrete to Continuous algorithm (DtC) … …Journal of Computer Science, Vol. 19, Issue 1 · 17 Jan 2023 · 5,908 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStock Return, Growth and Inflation in India: Analysis of Stochastic Seasonality, Impulse Response and Multivariate GARCHPanchanan DasThis study analyses the empirical interdependence among asset returns, industrial growth and inflation after controlling for interest rate by considering stochastic seasonality and conditional volatility with monthly time series in India. The HEGY unit root test suggests that industrial growth and inflation experience only stochastic trend and no persistent stochastic behaviour at any other seasonal frequencies, while stock return follows … …American Journal of Applied Sciences, Vol. 18, Issue 1 · 3 May 2021 · 7,348 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAnomaly Detection Algorithms for Streaming Data: Performance ComparisonZirije HasaniToday’s most of the data are streaming time-series data, where is very important anomaly detection over this data because gives significant information of possible critical situations. Detecting anomalies in big streaming data is yet difficult task because we have to process them in real time, even before they are stored and instantly alarm on potential threats. For real time streaming … …Journal of Computer Science, Vol. 16, Issue 7 · 18 Jul 2020 · 7,296 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAnalysis of Artificial Neural Network Point Forecasting Models and Prediction Intervals for Solar Irradiance EstimationAntônio Fabrício Guimarães de Sousa, Helaine Cristina Moraes Furtado, Wilson Negrão Macêdo, Anderson Alvarenga de Moura MenesesAn accurate knowledge on solar irradiance prediction is particularly required for proper development and planning of Photovoltaic (PV) energy systems. The main purpose of the present research is to assess the accuracy of Artificial Neural Networks (ANN) short-term forecast of univariate solar irradiance time series, with conventional point prediction and Prediction Intervals (PIs), comparing models. The Lower Upper Bound Estimation … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 3 · 17 Jul 2020 · 7,443 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 · 7,480 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA ModelQasem Abu Al-Haija, Qian Mao, Kamal Al NasrIn this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results … …Journal of Computer Science, Vol. 15, Issue 4 · 19 Apr 2019 · 8,607 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Automated Time-Shift Alignment Algorithm Based on Discrete to Continuous ApproachWajih Rhalem, Mourad Raji, Ahmed Hammouch, Jamal El MhamdiDealing with the variation in retention time and time series is one of the most popular challenges in the field of analysing data obtained from biological experiments. In most cases, this problem can lead to invalid conclusions. This work aims to propose a new method to cope with this problem by using the principle of the transition from discrete to … …Journal of Computer Science, Vol. 15, Issue 4 · 15 Apr 2019 · 7,772 accessesView PDFCite
Research ArticleOpen accessIn the keywordsForecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in MalaysiaWan Rozita Wan Mahiyuddin, Nur Izzah Jamil, Zamtira Seman et al.Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build Autoregressive Integrated Moving Average (ARIMA) model on the average of monthly ozone data … …American Journal of Environmental Sciences, Vol. 14, Issue 3 · 25 May 2018 · 8,946 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 · 7,891 accessesView PDFCite