Studies on pointwise prediction of nonlinear time series have revealed some distinguishing features of nonlinear prediction. Being signiicantly diierent from linear prediction, the prediction accuracy for nonlinear time series depends on the current position in the state space. Furthermore, small perturbation in current position can lead to considerable errors in nonlinear prediction (see Yao a...

With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interva...

data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

Random signal prediction is efficient for intelligent management and predictive diagnostics systems. Aim. The paper aims to analyse the error of random prediction. To develop recommendations selection extrapolator parameters. Methods . uses mathematics theory functions, formalization adopted in pulse systems, mathematical description extrapolators with Chebyshev polynomials orthogonal over a se...

Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; th...

This paper assesses the predictability of network traffic by considering two metrics: 1) how far into the future a traffic rate process can be predicted for a given error constraint; 2) what the minimum prediction error is over a specified prediction time interval. The assessment is based on two stationary traffic models: the Auto-Regressive Moving Average (ARMA) model and the Markov-Modulated ...

A method with best prediction properties that condenses information from all test days into measures of lactation yield and persistency has been proposed as a possible replacement for the test interval method and projection factors. The proposed method uses previously established correlations between individual test days and includes inversion of a matrix for each lactation. Milk weights that w...

The problem of energy saving in data centers has recently attracted significant interest within the research community, and the adaptive data center activation model has emerged as a promising technique to save energy. However, this model has not integrated adaptive activation of switches and hosts in data centers because of its complexity. This paper proposes an adaptive data center activation...

This paper presents a novel method for estimating “total” predictive uncertainty using machine learning techniques. By the term “total” we mean that all sources of uncertainty are taken into account, including that of the input and observed data, model parameters and structure, without attempting to separate the contribution given by these different sources. We assume that the model error, whic...