We develop a class of Minimum Distance Estimators for semi-strong Nonlinear ARMAX-Nonlinear GARCH processes. The estimators are asymptotically normal for possibly very heavy-tailed data due to underlying shocks and/or model parameter values. In particular we only impose trivial moment conditions on the GARCH errors, covering non-stationary GARCH. The MDE class is couched within a Method of Mome...

Our work presents a low-cost temperature acquisition, for incubator system, based on the Arduino hardware platform; both the hardware and software components are detailed, together with experimental evaluation. This system was designed to facilitate the process of identification and control of a temperature of premature infant incubator. The experimental evaluation revealed that this system is ...

This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mechanism. The solution to this problem requires forecasts of multiple processes: the participant’s energy production, day-ahead and balancing electricity prices, and the system length. By formulating the problem from a probabilistic ...

In order to exploit the nonlinear time-varying property of network traffic, the traffic flow from controlled sources is described by a fuzzy autoregressive moving-average model with auxiliary input (fuzzy ARMAX process), with the traffic flow from uncontrolled sources (i.e., cross traffic) being described as external disturbances. In order to overcome the difficulty of the transmission delay in...

This paper presents a two-step instrumental variable (IV) method to obtain the regularized and consistent parameter estimates of the Hammerstein ARMAX model based on the bilinear parameterized form. The two-step identification method consists of estimating the bilinear parameters in the first step, followed by parameter reduction in the second step. An iterative identification method is propose...