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Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial...
Two models are examined in this study: Vector Autoregressive Model (VAR) and Vector Error Correction Model (VECM). Based on three indices: S&P 500, Nikkei 225 (NIKKEI), and Morgan Stanley EAFE (MSCIEAFE or MSCI EAFE) Index, we implement VAR and VECM models, including the pre-estimation diagnostics, model estimation and interpretation and post-estimation tests, etc. By testing, we find that while...
This paper presents the development and implementation of an ARX (Auto-Regressive eXogenous) model for Model Predictive Control (MPC) in steam distillation extraction system. The mathematical model was developed using the system identification technique. The MPC is proposed as a controller in a way to regulate the system to maintain the optimum operation temperature besides minimizing the energy that...
Achieving accurate long range prediction and simulation performance in the identification of nonlinear polynomial input-output models requires both careful model selection and accurate parameter estimation. The simulation error minimization (SEM) identification approach has been shown to provide significant advantages over the standard prediction error minimization (PEM) approach for these modelling...
Software simulators remain several orders of magnitude slower than the modern microprocessor architectures they simulate. Although various reduced-time simulation tools are available to accurately help pick truncated benchmark simulation, they either come with a need for offline analysis of the benchmarks initially or require many iterative runs of the benchmark. In this paper, we present a novel...
Statistical theory is used to help select a margin level. This paper present a prudent margin-setting models to protect futures positions from extreme price movement. Five methods based GARCH models to estimate the current volatility are proposed to estimate Value at risk describing the tail of the conditional or unconditional distributions of two financial return series. Using backtesting of historical...
Efficient simulation models of nonlinear systems are useful for analyzing vibrational systems and designing and implementing control systems. Such models can also be used to produce estimates of the physical system parameters. NARMAX models can be used to simulate a wide range of nonlinear systems. Development of such models requires identification of the system nonlinearities and model structure...
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