nonlinear autoregressive
Electricity Consumption Forecasting Using Nonlinear Autoregressive with External (Exogeneous) Input Neural Network
... of autoregressive component, hence Nonlinear Autoregressive with External (Exogeneous) Input (NARX) Neural Network Time Series from Matlab R2018b was ...
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Factors influencing CO2 Emission in China: A Nonlinear Autoregressive Distributed Lags Investigation
... increase it. Once again, the results of these studies may be ambiguous due to use of different methods for different countries. Furthermore, the use linear econometric methodologies that impose similar size effect of ...
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A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks
... lows. We first briefly describe the nonlinear autoregressive model we use in Sec- tion 2. In Section 3 we present the results of numerical simulations performed in MatLab. In particular, among others, it is ...
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Economic growth, Environmental degradation and business cycles in Eswatini
... the nonlinear autoregressive distributive lag (NARDL) model to capture the long-run and short-run cointegration effects between economic activity and greenhouse gas (GHG) emissions over different phases of ...
RCS Calculation Using Hybrid FDTD-NARX Technique
... Abstract—This paper amalgamates two uncorrelated techniques namely finite difference time domain technique (FDTD) and nonlinear autoregressive with exogenous input (NARX) neural network to achieve a faster ...
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An Assymetric Evaluation of Oil Price- Inflation Nexus: Evidence from Nigeria
... This study examines the asymmetric oil price-inflation nexus in Nigeria spanning 2009Q1 to 2018Q4 using Nonlinear Autoregressive Distributed Lags (NARDL) model. The study finds an asymmetric long run ...
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Time series model for forecasting the number of new admission inpatients
... the nonlinear autoregressive neural network (NARNN) to the field of infectious diseases, for example forecasting the prevalence of schistosom- iasis in humans in Qianjiang City and Yangxin City, China [17, ...
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A Method of Hourly Load Forecasting by Time Series by Recycle of Predicted Values
... using nonlinear autoregressive neural network model is used to predict the load values as a time series from previous historical load ...values. Autoregressive neural network time series along with ...
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A. Artificial Neural Networks
... the proposed model works better than other models. Bisoi & Dash [24] proposed a simple IIR filter based dynamic neural network and an innovative optimized adaptive unscented Kalman filter for forecasting stock price ...
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Improved Prediction of Wind Speed using Machine Learning
... The prediction of wind speed plays a significant role in wind energy systems. An accurate prediction of wind speed is more important for wind energy systems, but it is difficult due to its uncertain nature. This paper ...
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Recurrent Neural Network Identification: Comparative Study on Nonlinear Process
... Here the NN regression modelswere applied for divided set data and it shows the modelling of second order nonlinear dynamic system. To select a model structure inside which we wish to search for a good model. The ...
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NARMAX_OLS Representation of a Semi-active Dynamic Leg Joint Model for a Paraplegic Subject Using Functional Electrical Stimulation
... produced. It is not possible to check all these models to find the most appropriate one. One does not know how many terms are necessary to model the principle dynamics of the system. This is similar to the problem that ...
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Forecasting ENSO with a smooth transition autoregressive model
... STAR modelling framework is that it it allows for non-discrete switching points between the extreme regimes, resulting in a potentially smooth transition between them. Since its introduction, the STAR modelling approach ...
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Informational Efficiency of Finance Stocks in Malaysia: A Two-Regime Nonlinear Threshold Autoregressive Approach
... this nonlinear threshold autoregressive model. A two-regime nonlinear threshold autoregressive approach is used to evaluating the random walk properties in two separate ...
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FCM BPSO: ENERGY EFFICIENT TASK BASED LOAD BALANCING IN CLOUD COMPUTING
... The research in [2][19] present that a hybrid technique can be used to further decomposes a time series data into linear and nonlinear form for further modeling. For example, for seasonal time series, firstly the ...
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Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
... The Autoregressive (AR), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models can be developed using time series approach for short term load forecasting ...
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Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey
... The latest version of GPP was published by TurkStat in 2001. Second, we thus take 2001 GPP as a base year. We normalize the provincial nightlights in 2001 to 100, and simply multiply the proportional growth of provincial ...
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An Application of PCA in ECG Classification with Cross - Validation
... become widely used tool in the assessment of the regular heart rate behaviour . Several techniques have been proposed for the investigation of HRV time series. Among them [2] used spectral methods based on Fast Fourier ...
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Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
... Due to the fact that water level forecasting involves a complex nonlinear data pattern; there are a lot of forecasting methods to improve the forecasting accuracy. It was reported that DID used general regression ...
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A New Nonlinear Unit Root Test with Fourier Function
... Enders and Granger (1998) demonstrate that the standard tests for unit root and cointegration all have lower power in the presence of misspecified dynamics. In the light of this information, it is important to determine ...
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