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 21 for “"Prediction interval"”.
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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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Modelling sea surface temperature using generalized additive models for location scale and shape by boosting with autocorrelation
… climate system of the earth. Modelling of and prediction from the SST data are challenging due to the fact that gaps in the data lead to incomplete information over time. Generalized additive models by boosting with location scale and shape (gamboostLSS) can be applied to overcome this problem. …
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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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Stormwater Runoff from Elevated Highways: Prediction of COD from Field Measurements and TSS
This proposed research focused on the prediction and identification of chemical oxygen demand (COD) concentrations in storm water runoff from elevated roadways, which transports a significant load of contaminants. The objective of this research was to develop a mathematical model to relate COD …
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Predicting Registered Health Information Administrator Examination Scores
… Administrator certification examination success prediction model and to establish a 95% Approximate Prediction Interval, while the remaining 79 records were used to validate the success prediction model. Ten independent variables were evaluated: race, ethnicity, mother tongue, age, four …
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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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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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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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Corporate Default Predictions and Methods for Uncertainty Quantifications
Regarding quantifying uncertainties in prediction, two projects with different perspectives and application backgrounds are presented in this dissertation. The goal of the first project is to predict the corporate default risks based on large-scale time-to-event and covariate data in the context of …
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Predictive model to estimate ionized calcium from routine serum biochemical profiles in dogs
… and diagnostic performance of piCal and its prediction interval (PI) were tested on 519 dogs via Bland-Altman analysis, Pearson’s R, and receiver operator characteristic (ROC) curves. The final model included creatinine, albumin, tCa, phosphorus, sodium, potassium, chloride, alkaline …
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Regression Analysis of Dissolved Heavy Metals in Storm Water Runoff from Elevated Roadways
This proposed research focused on the prediction and identification of dissolved heavy metals in storm water runoff from elevated roadways. Storm water runoff from highways transports a significant load of contaminants, especially heavy metals and particulate matter, to receiving waters. Heavy …
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Spatial structure and dynamics of the plant communities in a pro-grading river delta : Wax Lake Delta, Atchafalaya Bay, Louisiana
… and mean NDVI recovered to within the 95 percent prediction interval of the long-term trend by the following growing season. Following the historic 2011 Mississippi River flood, the area of the delta increased by nearly 5 km2. Greater increases in delta area occurred at higher water levels, …
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Deep Learning-based Time Series Forecasting: Models and Applications
… tremendous challenges to the processing and prediction of time series data. As a cutting-edge approach of artificial intelligence, deep learning has efficient automatic feature extraction and robust representation learning capabilities. Using deep learning to enhance time series forecasting …
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Determination of cardiac output across a range of values in horses by M-mode echocardiography and thermodilution
… with COecho measurements resulted in a broad 95% prediction interval such that COecho would have to change by more than 100% in order to be 95% confident that the determined value represents true hemodynamic change. COecho underestimated COTD by a mean of 10 +/- 6.3 l/min/450 kg. The large …
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Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads
… variance in SSC estimations and 50% narrower 95% prediction intervals for an annual loading estimate, when compared to a simple linear regression using a logarithmic transformation of the response and regressor (turbidity). Unexplained variance and prediction interval width were also reduced using …
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
… differed from conventional methods. The mean interval score was employed as a novel index to quantify prediction interval quality. Highly detailed simulations indicated that the model combination tool yielded superior or competitive prediction performance compared to alternative forecast …
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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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Role of metal ions in fermentative metabolism of yeast
… 5.63 (%v/v), respectively) were within their 95% prediction interval. Similarly for malt wort fermentations, models showed high coefficients o f determination (r<sup>2</sup> = 86.3, 81.9 and 81.9) under high, intermediate and low levels of Zn<sup>2+</sup>, respectively. It is therefore suggested …
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Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
… the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, …
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Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
… the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, …
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