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32 results for “time series regression”
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Research ArticleOpen accessPhrase in the titleNeural Networks Based Nonlinear Time Series Regression for Water Level Forecasting of Dungun RiverSiti Hajar Arbain, Antoni WibowoThe Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the … …Journal of Computer Science, Vol. 8, Issue 9 · 13 Aug 2012 · 7,792 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleModeling Pan Evaporation for Kuwait using Multiple Linear Regression and Time-Series TechniquesJaber AlmedeijThis study attempts to model evaporation for Kuwait under arid conditions by using a wide range of monthly evaporation data, varying from 0.1 to 40 mm/day, from January 1993 to July 2015. Owing to the reason that the well-known theoretical evaporation models presented in the literature have been justified for a much shorter data range, the paper adopts empirical approaches … …American Journal of Applied Sciences, Vol. 13, Issue 6 · 8 Jun 2016 · 7,089 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleβ-Spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series ErrorsJinhong You, Gemai Chen, Xian ZhouWe study the estimation problems for a partly linear regression model with a nonlinear time series error structure. The model consists of a parametric linear component for the regression coefficients and a nonparametric nonlinear component. The random errors are unobservable and modeled by a first-order Markov bilinear process. Based on a B-spline series approximation of the nonlinear component function, we … …American Journal of Applied Sciences, Vol. 2, Issue 9 · 30 Sep 2005 · 6,734 accessesView PDFCite
Review ArticleOpen accessEvery word in the titleThe use of Principal Component Regression and Time Series Analysis to Predict Nitrous Oxide Emissions in GhanaBenjamin Odoi, Lewis Brew, Christopher AttafuahThe disturbing pace of emanation of Nitrous Oxide (N2O) into the atmosphere and its calamitous impact on the environment, as monitored by many governmental agencies and researchers has become a wellspring of worry for many nations and therefore needs due attention. The study deployed nitrous oxide emissions from the three sectors to the total nitrous oxide emissions in Ghana over … …Energy Research Journal, Vol. 12, Issue 1 · 6 Jan 2021 · 8,597 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 keywordsModeling Tourist Arrivals Using Time Series Analysis: Evidence From AustraliaGurudeo AnandTularam, Victor Siew Howe Wong, Seyed AbdelhamidShobeiriNejadAustralian tourism has a logistic trend as Butler’s model shows. The stagnation has not been reached so opportunities exist to increase tourism. The logistic model predicts 7.2 million tourists in 2015 but time series models of ARIMA and VAR improve the prediction and explain the data. The ARIMA (2, 2, 2) fits well while the VAR lead to Granger causalities … …Journal of Mathematics and Statistics, Vol. 8, Issue 3 · 10 Sep 2012 · 7,857 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFuzzy Time Series: An Application to Tourism Demand ForecastingMuhammad Hisyam Lee, Maria Elena Nor, Suhartono et al.Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site … …American Journal of Applied Sciences, Vol. 9, Issue 1 · 24 Nov 2011 · 8,474 accessesView PDFCite
Research ArticleOpen accessIn the keywordsThe Applicability of the Short-run Phillips curve to NamibiaCyril A. OgbokorIn this research, we invoke linear and logarithmic regression models to empirically test the validity of the Short-run Phillips curve for Namibia by relying on macroeconomic time-series data running from 1991 to 2005.Our results offer some support for the presence of the phenomenon of stagflation in Namibia. This is rather contradictory to the underlying philosophy of the original Phillips curve. … …Journal of Social Sciences, Vol. 1, Issue 4 · 31 Dec 2005 · 7,211 accessesView PDFCite
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 abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDFCite