Auto-Regressive
Auto Regressive (AR) Models in Forecasting Methods
... ARIMA model is a generalization of an Autoregressive Moving Average (ARMA) model. This ARIMA (p,d,q) model, where p, d and q are non – negative integers that refer to the order of the auto regressive, ...
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Auto-Regressive Integrated Moving-Averages Model For Daily Rainfall Forecasting
... advance. Auto Regressive Integrated Moving Averages model (ARIMA model) is the time series model which studies the stationary series and hence is used for modelling and ...
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TIME WINDOW BASED AUTO REGRESSIVE HYBRID PSO FOR OPTIMAL CLOUD PACKAGE SELECTION
... based Auto-Regressive Hybrid PSO (TWARP) model that provides faster and more appropriate resource ...an auto-regression model to predict future requirements, a PSO-SA based optimal package selection ...
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Forecasting Gold Price with Auto Regressive Integrated Moving Average Model
... The present study forecasts the gold price of India by using auto regressive integrated moving average (ARIMA) model over a period of 25 years from July 1990 to February 2015. The study also uses mean ...
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The Antecedent of Domestic Investment in Indonesia: Auto Regressive Distributed Lag Approach
... Investment is one of the important factor in a developing country’s economy such as Indonesia which really needs the flow of investment to create jobs. The Investment comes from two sources, namely foreign investment and ...
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Auto Regressive Process (1) with Change Point: Bayesian Approch
... Keywords: Auto Regressive Model (1), Bayes Estimator, Change Point, Gibbs Sampling and MHRW Algorithm.. Tsurumi (1987) and Zacks (1983) are useful references on structural c[r] ...
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Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum
... Since this detector assumes that n(k) is an AR(M) process with Gaussian correlation function and the target has a Doppler of Ω, we call it Auto-Regressive, Gaussian correlation GLR (AR-GC-GLR(M,Ω))). The ...
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Predictability of Earthquake Occurrence Using Auto Regressive Integrated Moving Average (ARIMA) Model
... ARIMA model is considered one of the most widely used methodology in time series forecasting that aims to describe the autocorrelations in the data and use the ARIMA(p,d,q) notation. p denotes the order of auto ...
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Bootstrapping the portmanteau tests in weak auto regressive moving average models
... Overall, based on the bootstrapped critical values, the portmanteau tests, especially the weighted ones, give us a good indication in diagnostic checking of weak ARMA models, while the s[r] ...
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Commodity Prices and Macroeconomic Variables in India: An Auto Regressive Distributed Lag (ARDL) Approach
... Therefore, to know the effects of macroeconomic variables on commodity prices, we have constructed commodity price indexes, which are the combination of different groups of commodities l[r] ...
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Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model
... Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) of direct current motor,” Songklanakarin Journal of Science and Technology, ...“Nonlinear auto-regressive model structure selection ...
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Modeling and Forecasting of Ghana’s Inflation Volatility
... an auto-regressive AR(1) model for the squared ...The auto-correlation function (ACF) and partial auto-correlation function (PACF) charts shown in Figure 7 suggest that there is no significant ...
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Presenting a Model for Multiple-Step-Ahead-Forecasting of Volatility and Conditional Value at Risk in Fossil Energy Markets
... models, Auto-Regressive Integrated Moving Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) , Cointegrated Vector Auto Corresponding author; Email: ...
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Forecasting daily meteorological time series using ARIMA and regression models
... seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regres- sors in the form of Fourier terms and the time series regression, including trend and ...
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Advanced repetitive control of grid converters for power quality improvement
... Abbreviations Advanced Repetitive Control/Controller Alternating Current Auto Regressive Moving Average Band Width Conventional Repetitive Control/Controller Conventional Repetitive Cont[r] ...
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Vol 4, No 1 (2013)
... Three-tank (3T) system is the most representative didactical equipment used as a bench mark system for system modeling, identification and control. A real target representing 3T system has been used for generating data ...
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Performance Analysis of Epileptic EEG Expert System Using Scaled Conjugate Back Propagation Based ANN Classifier
... Autoregressive (AR) coe ffi cients describe the important features of EEG signals, the correct choice of model order is important. Too low and too high order gives the poor estimation of power spectral density (PSD) . In ...
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Induction motor modelling using fuzzy logic
... Fuzzy logic has been widely used in many engineering applications since this can overcome the limitations of conventional method of data analysis, modelling and system identification, and control system. The capability ...
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A non linear neural network technique for updating of river flow forecasts
... and easier to use than the other types of updating procedures. One of the simplest and most widely used model-output updating procedures is that which involves the forecasting of the errors in the simulated discharge ...
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Forecasting USD/IQD Future Values According to Minimum RMSE Rate
... The ARMA model is a statistical time series analysis technique based on discrete time dynamic modelling of the observations by using the weighted sum of previous r observations to predict the expected next observation, ...
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