This paper investigates the relationship between Bitcoin returns and frequency of daily abnormal over period from June 2013 to February 2020 using a number regression techniques model specifications including standard OLS, weighted least squares (WLS), ARMA ARMAX models, quantile regressions, Logit Probit piecewise linear non-linear regressions. Both in sample out-of-sample performance various ...

A small group of climate scientists and influencers have vigorously disputed the scientific consensus on change. They contributed to a belief system that has impeded policy actions reduce emissions. accept more CO2 in atmosphere consequences for but strongly deny magnitude effect is significant. Using hourly data from Mauna Loa Observatory Hawaii, this article examines whether temperature at ne...

Thermoelectric cooling (TEC), in particular, can be combined with a heat sink for local cooling, but they also integrated into electronic chips point-to-point cooling. The study aims to develop dynamic model of system TEC glass window. main target this is TEC. black box modelling approach producing mathematical was selected based on the ARMAX and ARX that corresponds actual state system. best f...

This paper presents a methodology to create a recursive autoregressive model capable of estimating modal parameters of the recorded crash pulse which can be further used as parameters characterizing a physical model (spring-massdamper model). Such a viscoelastic system with nonlinear parameters estimated by RARMAX model yields results which closely follow the reference vehicle’s kinematics. Thi...

This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed fr...

This study investigates the selection of an appropriate low flow forecast model for the Meuse River based on the comparison of output uncertainties of different models. For this purpose, three data driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is ass...

This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiment subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all expe...

The commonly made assumption of Gaussian noise is an approximation to reality. In this paper, influence function, an analysis tool in robust statistics, is used to formulate a recursive solution for the filtering of the ARMAX process with generalized t-distribution noise. By being a superset encompassing Gaussian, uniform, t, and double exponential distributions, generalized t-distribution has ...