Financial options and statistical prediction intervals

Title Prediction Intervals for Three Basic Statistical Models

Prediction Intervals

Computing prediction intervals (P.I.s) is an important part of the forecasting process intended s i to indicate the likely uncertainty in point forecasts. The commonest method of calculating P.I. s to use theoretical formulae conditional on a best-fitting model. If a normality assumption is t o used, it needs to be checked. Alternative computational procedures that are not so dependen n a fitte...

Prediction Intervals for Performance Prediction

The Predictive Performance Equation (PPE) is a mathematical model of learning and forgetting developed to capture performance effectiveness across training histories, and to generate precise, quantitative point predictions of performance by extrapolating the unique mathematical regularities indicative of the learner. This equation is implemented in the Predictive Performance Optimizer (PPO) cog...

Semiparametric Bootstrap Prediction Intervals in time Series

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...

Practical Confidence and Prediction Intervals

We propose a new method to compute prediction intervals. Especially for small data sets the width of a prediction interval does not only depend on the variance of the target distribution, but also on the accuracy of our estimator of the mean of the target, i.e., on the width of the confidence interval. The confidence interval follows from the variation in an ensemble of neural networks, each of...