Considered are general, pure linear prediction schemes, where the prediction of the current sample is based on IIR-filtered past samples. Properties of these schemes are discussed, in particular, the whitening property, the realisation of the synthesis filter and its stability. The realisation problem as occurring in warped linear prediction is solved. Furthermore, it is proved that, at least f...

The problems of constructing prediction intervals for the binomial and Poisson distributions are considered. Available approximate, exact and conditional methods for both distributions are reviewed and compared. Simple approximate prediction intervals based on the joint distribution of the past samples and the future sample are proposed. Exact coverage studies and expected widths of prediction ...

We consider the prediction problem of a continuous-time stochastic process on an entire time-interval in terms of its recent past. The approach we adopt is based on the notion of autoregressive Hilbert processes that represent a generalization of the classical autoregressive processes to random variables with values in a Hilbert space. A careful analysis reveals, in particular, that this approa...

In forecasting a ®nancial time series, the mean prediction can be validated by direct comparison with the value of the series. However, the volatility or variance can only be validated by indirect means such as the likelihood function. Systematic errors in volatility prediction have an `economic value' since volatility is a tradable quantity (e.g. in options and other derivatives) in addition t...

Prediction on the basis of censored data is very important topic in many fields including medical and engineering sciences. In this paper, based on progressive Type-II right censoring scheme, we will discuss Bayesian two-sample prediction. A general form for lifetime model including some well known and useful models such as Weibull and Pareto is considered for obtaining prediction bounds as wel...

Abstract: Frequency prediction after a disturbance has received increasing research attention given its substantial value in providing a decision-making foundation in power system emergency control. With the advancing development of machine learning, analysis power systems with machine-learning methods has become completely different from traditional approaches. In this paper, an ensemble algor...

I present algorithms for automated long-term behavior prediction which can recognize when a simulation has run long enough to produce a representative behavior sample, characterize the behavior, and determine whether this behavior will continue forever, or eventually terminate or otherwise change its character. I have implemented these algorithms in a working program which does longterm behavio...

This paper evaluates the predictability of monthly stock return using out-of-sample (multi-step ahead and dynamic) prediction intervals. Past studies have exclusively used point forecasts, which are of limited value since they carry no information about the intrinsic predictive uncertainty associated. We compare empirical performances of alternative prediction intervals for stock return generat...

A sample of N units is taken from a population consisting of an unknown number of species. We are interested in estimating the number of species and the prediction function for future sampling. The prediction function is defined as the expected number of new species that will be found if an additional sample of size tN is taken, for any positive real number t. In this paper we point out that an...