Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 38 for “"Prediction intervals"”.
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Uncertainty in Neural Networks; Bayesian Ensembles, Priors & Prediction Intervals
… is the communication of uncertainty in a NN's predictions and decisions. In applications such as healthcare recommendation or heavy machinery prognostics, it is vital that AI systems be aware of and express their uncertainty – this creates safer, more cautious, and ultimately more useful …
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Improving ensembles and prediction intervals for machine learning on data streams
… more flexible selection criteria. The Adaptive Prediction Interval (AdaPI) framework provides robust uncertainty quantification by adaptively adjusting prediction intervals based on historical coverage, ensuring reliability in streaming regression. To evaluate prediction intervals holistically, …
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Sieve bootstrap based prediction intervals and unit root tests for time series
… to empirical time series, to obtaining prediction intervals for integrated, long-memory, and seasonal time series as well as constructing a test for seasonal unit roots, is considered. The advantage of this resampling method is that it does not require knowledge about the underlying …
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A modified approach for obtaining sieve bootstrap prediction intervals for time series
… traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres assumes that the underlying distribution of the innovations is Gaussian. It is well known that deviations from this assumption can lead to prediction intervals with poor coverage. Nonparametric …
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Reliable Prediction Intervals and Bayesian Estimation for Demand Rates of Slow-Moving Inventory
Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a …
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… overview of different nonparametric methods for prediction interval estimation and investigate how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear regression settings. Using …
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Prediction interval modeling using Gaussian process quantile regression
In this thesis a methodology to construct prediction intervals for a generic black-box point forecast model is presented. The prediction intervals are learned from the forecasts of the black-box model and the actual realizations of the forecasted variable by using quantile regression on the …
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INFERENCE AFTER VARIABLE SELECTION
… + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most results are for n/p large, but prediction intervals are developed that may increase in average length …
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Quantile-based methods for prediction, risk measurement and inference
… and practical quantile methods in addressing prediction, risk measurement and inference problems. From a prediction perspective, a problem of creating model-free prediction intervals for a future unobserved value of a random variable drawn from a sample distribution is considered. With the …
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Development and applications of neural networks for economic forecasting
… neural networks which allows the generation of prediction intervals for forecasts, and present variants to the architecture which improve the accuracy of these prediction intervals. In Chapter 3, we focus on confidence intervals, and present the first simulation study of the suitability of …
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The Detoxification of Petroleum Contaminated Coastal Plain Sandy Soil Using an Amended Vermicomposting Approach
… 3. In addition, the Lower 95% and Upper 95% prediction intervals and the Lower 99% and Upper 99% prediction intervals for Treatment 3 were the largest among the test and control soils. This suggests the amended vermicomposting treatment approach has merit and further studies should be …
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Essays on Time Series Modeling
… method outperforms the alternatives, yielding prediction intervals whose empirical levels and nominal levels match well, even under a misspecified conditional variance. Our second important result in this chapter is that the GARCH(1,1) specification is reliable for building prediction intervals …
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Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection
… projects: 1) construction of simultaneous prediction intervals/bounds for at least k out of m future observations; 2) semi-parametric degradation model for accelerated destructive degradation test (ADDT) data; and 3) spatial variable selection and application to Lyme disease data in …
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Robust Inference via Optimal Transport Ambiguity Sets
… via Wasserstein ambiguity sets. Split conformal prediction, hereafter referred to as conformal prediction, offers a powerful framework for quantifying predictive uncertainty by constructing prediction intervals with finite-sample, distribution-free guarantees. Despite its widespread success, …
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Hierarchical forecasting of electricity demand in South Africa
… In order to combine forecasts and compute the prediction intervals for the developed models the quantile regression averaging (QRA) and linear regression (LR) is used. The best set of forecasts is selected based on the prediction interval normalised average width (PINAW) and pinball loss. The …
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Quantile regression and survival analysis
… of regression quantiles to construct confidence intervals and confidence bands for conditional quantiles and prediction intervals for future response variables under homoscedastic linear models and heteroscedastic linear models is proposed. Comparison of the direct method with the studentization …
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Validating Forecasting Strategies of Simple Epidemic Models on the 2015-2016 Zika Epidemic
… training/testing split in data and associated prediction intervals. Fore- casting accuracy was evaluated using five statistical performance metrics. Early into the epidemic, phenomenological models - like the generalized logistic model - resulted in more accurate forecasts. However, as the …
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Probabilistic Models for Human Migration Forecasting and Residency Imputation
… adjusts net migration rates, offering narrower prediction intervals and more accurate projections of population change, especially for aging populations. Finally, I improve the Person-Place Model (PPM), a key tool used by countries without population registers for census and intercensal …
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On the Viability of Quantitative Assessment Methods in Software Engineering and Software Services
… problems are resolved efficiently. An incident prediction method is needed for planning staffing levels. The potential value of a solution to this problem is important to an IT service provider since software failures are inevitable and their timing is difficult to predict. In this research, a …
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Analysis and uncertainty of airport pushback rate control policies
… varying two policy parameters, the length of the prediction interval and the number of prediction intervals, under several types of uncertainty, including the departure schedule and arrival rate. As will be shown, each policy results in significant taxi-out time reductions, saving airlines at …
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