499 research outputs found
A review of multi-component maintenance models with economic dependence
In this paper we review the literature on multi-component maintenance models with economic dependence. The emphasis is on papers that appeared after 1991, but there is an overlap with Section 2 of the most recent review paper by Cho and Parlar (1991). We distinguish between stationary models, where a long-term stable situation is assumed, and dynamic models, which can take information into account that becomes available only on the short term. Within the stationary models we choose a classification scheme that is primarily based on the various options of grouping maintenance activities: grouping either corrective or preventive maintenance, or combining preventive-maintenance actions with corrective actions. As such, this classification links up with the possibilities for grouped maintenance activities that exist in practice
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Probabilistic time series forecasting predicts the conditional probability
distributions of the time series at a future time given past realizations. Such
techniques are critical in risk-based decision-making and planning under
uncertainties. Existing approaches are primarily based on parametric or
semi-parametric time-series models that are restrictive, difficult to validate,
and challenging to adapt to varying conditions. This paper proposes a
nonparametric method based on the classic notion of {\em innovations} pioneered
by Norbert Wiener and Gopinath Kallianpur that causally transforms a
nonparametric random process to an independent and identical uniformly
distributed {\em innovations process}. We present a machine-learning
architecture and a learning algorithm that circumvent two limitations of the
original Wiener-Kallianpur innovations representation: (i) the need for known
probability distributions of the time series and (ii) the existence of a causal
decoder that reproduces the original time series from the innovations
representation. We develop a deep-learning approach and a Monte Carlo sampling
technique to obtain a generative model for the predicted conditional
probability distribution of the time series based on a weak notion of
Wiener-Kallianpur innovations representation. The efficacy of the proposed
probabilistic forecasting technique is demonstrated on a variety of electricity
price datasets, showing marked improvement over leading benchmarks of
probabilistic forecasting techniques
Towards Handling Uncertainty in Prognostic Scenarios: Advanced Learning from the Past
In this report we introduce the paradigm of learning from the past which is realized in a controlled prognostic context. It is a data-driven exploratory approach to assessing the limits to credibility of any expectations about the system’s future behavior which are based on a time series of a historical observations of the analyzed system. This horizon of the credible expectations is derived as the length of explainable outreach of the data, that is, the spatio-temporal extent which, in lieu of the knowledge contained in the historical observations, we are justified in believing contains the system’s future observations. Explainable outreach is of practical interest to stakeholders since it allows them to assess the credibility of scenarios produced by models of the analyzed system. It also indicates the scale of measures required to overcome the system’s inertia. In this report we propose a method of learning in a controlled prognostic context which is based on a polynomial regression technique. A polynomial regression model is used to understand the system’s dynamics, revealed by the sample of historical observations, while the explainable outreach is constructed around the extrapolated regression function. The proposed learning method was tested on various sets of synthetic data in order to identify its strengths and weaknesses, and formulate guidelines for its practical application. We also demonstrate how it can be used in context of earth system sciences by using it to derive the explainable outreach of historical anthropogenic CO2 emissions and atmospheric CO2 concentrations. We conclude that the most robust method of building the explainable outreach is based on linear regression. However, the explainable outreach of the analyzed datasets (representing credible expectations based on extrapolation of the linear trend) is rather short
Asymptotic behaviour of mild solution of nonlinear stochastic partial functional equations
This paper presents conditions to assure existence, uniqueness and stability for impulsive neutral stochastic integrodifferential equations with delay driven by Rosenblatt process and Poisson jumps. The Banach fixed point theorem and the theory of resolvent operator developed by Grimmer [R.C. Grimmer, Resolvent operators for integral equations in a Banach space, Trans. Am. Math. Soc., 273(1):333–349, 1982] are used. An example illustrates the potential benefits of these results
A stochastic maximum principle for general mean-field backward doubly stochastic control
In this paper we study the optimal control problems of general MckeanVlasov for backward doubly stochastic differential equations (BDSDEs), in which the coefficients depend on the state of the solution process as well as of its law. We establish a stochastic maximum principle on the hypothesis that the control field is convex. For example, an example of a control problem is offered and solved using the primary result.Publisher's Versio
A nonparametric copula based test for conditional independence with applications to Granger causality
nonparametric tests, conditional independence, Granger non-causality, Bernstein density copula, bootstrap, finance, volatility asymmetry, leverage effect, volatility feedback effect, macroeconomics
- …