The price of crude oil soared after the outbreak Russia-Ukraine conflict. However, China's stock market, by contrast, continues to fall. This paper studies impact on dynamic China’s market. We build an ARMAX model and ARMA-GARCH analyze whether there are connections between finds that futures rate return is positively correlated with Shenzhen Component index Shanghai return. Also, has a signifi...

This paper extends current theory on the identification and estimation of vector time series models to nonstationary processes. It examines the structure of dynamic simultaneous equations systems or ARMAX processes that start from a given set of initial conditions and evolve over a given, possibly infinite, future time horizon. The analysis proceeds by deriving the echelon canonical form for su...

Forecasting of machine outages have been actively pursued in the manufacturing industries to ensure that maintenance is carried out only when required. In this paper, we propose a precognitive maintenance framework based on mixed timeand condition-based models to predict both machine degradation stage and wear. The decision-making framework is based on stage classification using Support Vector ...

The identification of dynamic processes can be performed by means of different classes of models relying on different stochastic environments to describe the misfit between the model and process observations. This paper introduces a new class of models by considering additive error terms on the observations of the input and output of ARARX models and proposes a three–step identification procedu...

This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and outpu...

We analyse the relationship between a mobile robot’s perception, motion and position (i.e. we address the problem of sensor-based self-localisation), by constructing transparent, linear polynomial models, using an ARMAX process which expresses the interdependency between motion, perception and location. Our models show that in sensor-based localisation there is a clear and measurable trade-off ...

We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems. The impulse responses are modeled as Gaussian processes whose autocovariances encode the BIBO stability constraint, as defined by the recently introduced “Stable Spline kernel”. Sparse solutions are obtained by placing exponential hyperpriors on the scale factors of such kernels. Numerical exp...