Non-Linear Auto-Regressive with Exogenous
Multi-Layer Perceptron (MLP)-Based Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Stock Forecasting Model
... The internet search queries were used to predict stock market volatility. Volatility is an indicator of strong market movements. The researchers theorized that the internet search queries indicated investors’ interest ...
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Stability and Profitability in the Chinese Banking Industry: evidence from an auto-regressive-distributed linear specification
... Abstract The important role played by the Chinese commercial banks in the development of China’s economy makes the government and banking regulatory authority give great concern about the performance of Chinese ...
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Auto Regressive (AR) Models in Forecasting Methods
... Box and Jenkins recommend differencing a time series, rather than detrending by regressing on a time trend, to remove the trend and achieve stationary. This approach views the trend in a series as erratic and not very ...
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Auto-regressive moving average analysis of linear and discontinuous models of human balance during quiet standing
... keywords: linear stochastic model, switched model, ARMA fitting, posturographic ...being linear, continuous and time invariant (LTI), or whether an intermittent (switched/containing dead-zones) control ...
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Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum
... The AR-GC-GLR detector doesn’t need to have a separate set of data vectors (secondary data) for adaptation and can estimate the statistics of the environment using only the data under test. This property is very ...
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Application of Spatial Auto-Regressive Model for Determining Urban Land Market
... Spatial Auto-Regression Analysis In order to use the data in the Spatial Autoregression analysis, probability density functions of transacted land price must be specified and a distribution table needs to be ...
Auto-Regressive Integrated Moving-Averages Model For Daily Rainfall Forecasting
... Partial Auto Correlator of a particular order. I. Auto Regressive Moving Average Model (ARMA) To model data and further forecast AR and MA models are combined to produce ...
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Economic growth and electricity consumption: Auto regressive distributed lag analysis
... 3. Econometric methodology In this paper, the ARDL approach to cointegration involves two steps for estimating a long-run rela- tionship. The first step is to investigate the existence of a long-run relationship among ...
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Instantaneous frequency estimation of multicomponent non- stationary signals using Fourier Bessel series and Time-Varying Auto Regressive Model
... component non stationary signals using Fourier Bessel Series and Time– Varying Auto Regressive (FB-TVAR) ...multi-component non stationary signal into a number of mono- component signals and ...
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Power Consumption Short Term Forecast Using Signal Auto Regressive Method
... is non-stationary, then first it should be converted to stationary ...with non-stationary averages, but through a different method by introducing operator ...
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Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data
... and non-linear in nature, ADAPT presents the opportunity for complex diffusion biomarkers to be obtained by making no prior assumptions about the nature of the data nor does it require any multistep fitting ...
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Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter
... 6. Conclusion Taking into account the nonlinear character of the textured images, a new nonlinear exponential adaptive 2-D filter for texture characterization is proposed in this paper. The nonlinear filter coefficients ...
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TRADING SYSTEM FOR AUSTRALIAN DOLLAR USING MULTIPLE MOVING AVERAGES AND AUTO- REGRESSIVE MODELS.
... The standard statistical analysis is extended through the use of bootstrap techniques and still found the techniques to be worth considering. The results of their research are consistent with the popular belief that ...
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Predictability of Earthquake Occurrence Using Auto Regressive Integrated Moving Average (ARIMA) Model
... With the abovementioned, global researches about earthquakes were conducted to intensify our understanding and have a grasp on the techniques to foresee them [8]. Various studies about earthquake predictability were ...
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Islamic REIT response to macroeconomic factors: a markov regime switching auto regressive approach
... with non-shariah compliant REITs listed on the London Stock Exchange, Singapore Stock Exchange and Kuala Lumpur Stock Exchange (Malaysia) against the movement of US inflation and interest ...switching auto ...
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A Comparison between LARS and LASSO for Initialising the Time-Series Forecasting Auto-Regressive Equations
... c 2011 Published by Elsevier Ltd. Keywords: LASSO, LARS, OLS, AR(n) models, Time Series 1. Introduction The variable selection problem arises in many practical situations when, out of a set of variables involved in a ...
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Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies
... vector auto-regressive (VAR) models, are generally required to satisfy a weak or second order ...the auto-covariance is dependent on the time lag only ...of non-stationary data may be ...
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International trade and economic growth in Nigeria: An auto regressive and distributed LAG bound test approach
... domestic industries that were set up to produce import substitutes. The customs tariff structure was deliberately discriminatory, biased in favour of capital goods and raw materials. Items considered as luxury goods were ...
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Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies
... vector auto-regressive (VAR) models, are generally required to satisfy a weak or second order ...the auto-covariance is dependent on the time lag only ...of non-stationary data may be ...
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Generalized Auto-regressive Conditional Heteroscedastic Modeling of the Volatility of Stock Returns in the Nairobi Securities Exchange
... A study by (Akgiray, 1989) uses a GARCH(1,1) model to investigate the time series properties of the stock returns and reports GARCH to be the best of several models in describing and forecasting stock market volatility. ...
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