Long-term prediction intervals of economic time series
Sign Up to like & getrecommendations! Published in 2019 at "Empirical Economics"
DOI: 10.1007/s00181-019-01689-2
Abstract: We construct long-term prediction intervals for time-aggregated future values of univariate economic time series. We propose computational adjustments of the existing methods to improve coverage probability under a small sample constraint. A pseudo-out-of-sample evaluation shows… read more here.
Keywords: term prediction; economic time; prediction intervals; long term ... See more keywords
An analysis pipeline for estimating true intake from repeated measurements with random errors
Sign Up to like & getrecommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2018.1429624
Abstract: ABSTRACT The accurate estimation of an individual's usual dietary intake is an important topic in nutritional epidemiology. This paper considers the best linear unbiased predictor (BLUP) computed from repeatedly measured dietary data and derives several… read more here.
Keywords: prediction intervals; pipeline estimating; analysis pipeline; true intake ... See more keywords
Prediction intervals reporting in orthodontic meta-analyses.
Sign Up to like & getrecommendations! Published in 2021 at "European journal of orthodontics"
DOI: 10.1093/ejo/cjab037
Abstract: BACKGROUND A prediction interval represents a clinical interpretation of heterogeneity. The aim of this study was to determine the prevalence of prediction interval reporting in orthodontic random effect meta-analyses. The corroboration between effect size estimates… read more here.
Keywords: meta analyses; prediction intervals; prediction; prediction interval ... See more keywords
A Construction Approach to Prediction Intervals Based on Bootstrap and Deep Belief Network
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2938214
Abstract: Traditional neural networks (NNs) have been widely used in prediction intervals (PIs) construction method, with many improved models have been proposed. However, there are not satisfactory prediction results when dealing with some complex prediction problems… read more here.
Keywords: pis; construction; prediction intervals; prediction ... See more keywords
A Granular Computing-Based Hybrid Hierarchical Method for Construction of Long-Term Prediction Intervals for Gaseous System of Steel Industry
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2983446
Abstract: Byproduct gaseous energy is crucial to the iron-steel manufacturing process, where the tendencies of its generation and consumption can be deemed as a significant reference for scheduling production and decision-making. Besides the requirements imposed on… read more here.
Keywords: granular computing; computing based; prediction intervals; long term ... See more keywords
Hierarchical-Bayesian-Based Sparse Stochastic Configuration Networks for Construction of Prediction Intervals.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3053306
Abstract: To address the architecture complexity and ill-posed problems of neural networks when dealing with high-dimensional data, this article presents a Bayesian-learning-based sparse stochastic configuration network (SCN) (BSSCN). The BSSCN inherits the basic idea of training… read more here.
Keywords: sparse stochastic; prediction intervals; prediction; based sparse ... See more keywords
Optimal Nonparametric Prediction Intervals of Electricity Load
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Power Systems"
DOI: 10.1109/tpwrs.2020.2965799
Abstract: A novel machine learning based mixed integer programming model is developed for the optimal nonparametric prediction intervals (PIs) of electricity load, which minimizes interval width subject to target hit probability constraint. Binary variables are employed… read more here.
Keywords: electricity load; optimal nonparametric; prediction intervals; nonparametric prediction ... See more keywords
Prediction intervals with random forests
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280219829885
Abstract: The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates… read more here.
Keywords: prediction intervals; forest method; random forests; performance ... See more keywords
Application of prediction intervals to the interpretation of the robustness study of a UHPLC method for the separation of cannabinoids.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of pharmaceutical and biomedical analysis"
DOI: 10.2139/ssrn.4134171
Abstract: Design of Experiments (DoE) is a well-established tool used for analytical methods robustness studies, because of its ability to assess the effect of a great number of factors in a minimal number of experiments. However,… read more here.
Keywords: information; effect; prediction; robustness ... See more keywords
Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach
Sign Up to like & getrecommendations! Published in 2019 at "Energies"
DOI: 10.3390/en12244713
Abstract: Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at… read more here.
Keywords: prediction intervals; using measured; measured solar; prediction ... See more keywords