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51 results for “Autoregressive Model”
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Research ArticleOpen accessEvery word in the titleTrade-GDP Nexus in Iran: An Application of the Autoregressive Distributed Lag (ARDL) ModelMosayeb Pahlavani, Ed Wilson, Andrew C. WorthingtonThis study employed annual time series data (1960-2003) and unit root tests with multiple breaks to determine the most likely times of structural breaks in major factors impacting on the trade-GDP nexus in Iran We found, inter alia, that the endogenously determined structural breaks coincided with important events in the Iranian economy, including the 1979 Islamic revolution and the outbreak … …American Journal of Applied Sciences, Vol. 2, Issue 7 · 31 Jul 2005 · 8,516 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAutoregressive Integrate Mean Average as Approaches for Modeling Energy Production in the Electricity Network by Seasons of the Electric Energy Company in Lomé, TogoApaloo Bara Komla Kpomonè et al.The work accumulated in this article presents the results of modeling the tduction of electrical energy for the CEET network in Lomé, Togo, taking into account the seasons of the area. The objective is to evaluate the forecasts over the periods in relation to the seasons of the year. Four seasons are detected, namely: Long dry season, long rainy season, … …American Journal of Engineering and Applied Sciences, Vol. 17, Issue 4 · 20 Dec 2024 · 3,922 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDAILY CRUDE OIL PRICE FORECASTING MODEL USING ARIMA, GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTIC AND SUPPORT VECTOR MACHINESRana Abdullah Ahmed, Ani Bin ShabriCrude oil price forecasting is gaining increased interest globally. This interest is due mainly to the economic value attached to the product. For this reason, new forecasting methods are proposed in the literature. This paper proposes a novel technique for forecasting crude oil price based on Support Vector Machines (SVM). The study adopts the data on crude oil price of … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 11 Jan 2014 · 8,725 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSeasonal 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 keywordsModeling Positive Time Series Data: A Neglected Aspect in Time Series CoursesMaman A. Djauhari, Lee Siaw Li, Rohayu Mohd SallehSomething has been forgotten in time series courses, in particular, when dealing with positive datasets. To describe the pattern hidden in this type of datasets, before we use a sophisticated method of modeling such as Autoregressive Integrated Moving Average (ARIMA), we propose to check first whether the data represent a Geometric Brownian Motion (GBM) process. If it is affirmative, unlike … …American Journal of Applied Sciences, Vol. 13, Issue 7 · 1 Jul 2016 · 7,533 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Evolving Autoregressive Predictor for Time Series ForecastingDe Z. Li, Wilson Wang, Fathy IsmailAutoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 1 · 8 Apr 2015 · 6,965 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMAXIMUM LIKELIHOOD ESTIMATION FOR SPATIAL DURBIN MODELRokhana Dwi Bekti, Anita Rahayu, SutiknoSpatial Durbin Model (SDM) is one method of spatial autoregressive. This model was developed because the dependencies in the spatial relationships not only occur in the dependent variable, but also on the independent variables. In the assessment of parameter estimation, the process is carried out by Maximum Likelihood Estimation (MLE). This estimation can be approximation by Spatial Autoregressive Models (SAR). … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 15 Jun 2013 · 8,636 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGENERALIZED LINEAR MIXED MODELS WITH SPATIAL RANDOM EFFECTS FOR SPATIO-TEMPORAL DATA: AN APPLICATION TO DENGUE FEVER MAPPINGKrisada Lekdee, Lily IngsrisawangThe Generalized Linear Mixed Models (GLMMs) with spatial random effects for spatio-temporal data are proposed. A hierarchical Bayesian method is used for parameter estimation. The random effects are assumed to be normally distributed and the spatial random effects are assumed to be proper Conditional Autoregressive (CAR) models. The proposed models are applied to Dengue fever data in Northern Thailand, including … …Journal of Mathematics and Statistics, Vol. 9, Issue 2 · 13 May 2013 · 8,065 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTrend and Pattern in Average Monthly Maximum Temperatures in Thailand from 1986 to 2015Manachai Rodchuen, Samruam Chongcharoen, Walaithip BunyatisaiThis research aims to study the trends and patterns of average maximum temperature change data of monthly surface in Thailand during the 30 year-period from 1986 to 2015 that has already eliminated seasonal variations and autocorrelation. The data set obtained from Climatological center, Meteorological Development Division, Thailand Meteorological Department for 73 completed recording stations. The climatically regions of Thailand meteorology … …American Journal of Applied Sciences, Vol. 17, Issue 1 · 25 Mar 2020 · 7,942 accessesView PDFCite
Research ArticleOpen accessIn the abstractTime Series Analysis for Predicting Tea Harvest Yield: A SARIMAX-Based ApproachPallavi Nagpal, Deepika Chaudhary, Jaiteg SinghPrecise prediction of tea yield is crucial for both agricultural planning and economic forecasting. Projection of the future trends, which rely on present and historical data, is the process termed 'forecasting'. Yield prediction is fundamental for research and development. By gathering the yield data over historic times, the researchers can make their work more valuable by predicting the yield patterns. … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,891 accessesView PDFCite