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13 results for “Autoregressive Process”
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Research ArticleOpen accessEvery word in the titleModeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity ProcessesAyodele Abraham Agboluaje, Suzilah bt Ismail, Chee Yin YipThis study has been able to reveal that the Combine White Noise model outperforms the existing Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Moving Average (MA) models in modeling the errors, that exhibits conditional heteroscedasticity and leverage effect. MA process cannot model the data that reveals conditional heteroscedasticity and GARCH cannot model the leverage effect also. The standardized residuals of GARCH … …American Journal of Applied Sciences, Vol. 12, Issue 11 · 20 Nov 2015 · 6,721 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 keywordsNew Procedure to Improve the Order Selection of Autoregressive Time Series ModelAli Hussein Al-MarshadiProblem statement: We propose new approach could be used to guide the selection of the "true" order of autoregressive model for different sample size. Approach: We used simulation study to compare four model selection criteria with and without the help of the new approach. The comparison of the four model selection criteria was in terms of their percentage of number … …Journal of Mathematics and Statistics, Vol. 7, Issue 4 · 14 Sep 2011 · 7,259 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAdaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video TrafficT. Raghuveera, P. V.S. Kumar, K. S. EaswarakumarProblem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, resource … …Journal of Computer Science, Vol. 7, Issue 6 · 14 Jun 2011 · 7,013 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
Research ArticleOpen accessIn the abstractModeling 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 abstractIDENTIFICATION OF PERIODIC AUTOREGRESSIVE MOVING-AVERAGE TIME SERIES MODELS WITH RHazem I. El Shekh Ahmed, Raid B. Salha, Diab I. AL-AwarPeriodic autoregressive moving average PARMA process extend the classical autoregressive moving average ARMA process by allowing the parameters to vary with seasons. Model identification is the identification of a possible model based on an available realization, i.e., determining the type of the model with appropriate orders. The Periodic Autocorrelation Function (PeACF) and the Periodic Partial Autocorrelation Function (PePACF) serve as … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 15 Sep 2014 · 12,904 accessesView PDFCite
Research ArticleOpen accessIn the abstractMAXIMUM 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 abstractPredicting Battery Charge Depletion in Wireless Sensor Networks Using Received Signal Strength IndicatorInacio Henrique Yano, Vitor ChavesDe Oliveira, Eric Alberto de Mello Fagotto et al.This article aims to identify an adequate mathematical model to predict battery power depletion at the nodes of a Wireless Sensor Network (WSN), by analyzing the Received Signal Strength Indicator (RSSI). Six general models were tested, the simplest Average model, Linear Regression model, Autoregressive (AR) models and Autoregressive Moving Average (ARMA) models.The selected model (AR) presented a low absolute mean … …Journal of Computer Science, Vol. 9, Issue 7 · 12 Jun 2013 · 7,239 accessesView PDFCite
Research ArticleOpen accessIn the abstractNeural 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