Prediction Intervals Research Articles

Lithium Exposure and Risk of Major Neurocognitive Disorders: A Systematic Review and Meta-analysis.

Published studies on the association between lithium use and the decreased risk of major neurocognitive disorders (MNCDs) have shown disparities in their conclusions. We aimed to provide updated evidence of this association. A comprehensive literature search was performed in PubMed, EMBASE, and Cochrane Library from inception until August 31, 2023. All the observational studies evaluating the association between lithium use and MNCD risk were eligible for inclusion. Pooled odds ratios (ORs) and 95% prediction intervals were computed using random-effects models. Eight studies with 377,060 subjects were included in the analysis. In the general population on the association between lithium use versus nonuse and dementia, the OR was 0.94 (95% confidence interval [CI] = 0.77-1.24). Further analysis also demonstrated that lithium use was not associated with an increased risk of Alzheimer's disease (OR = 0.69, 95% CI: 0.31-1.65). When the analysis was restricted to individuals with bipolar disorder to reduce the confounding by clinical indication, lithium exposure was also not associated with a decreased risk of MNCD (OR = 0.9, 95% CI = 0.71-1.15). The results of this systematic review and meta-analysis do not support a significant association between lithium use and the risk of MNCD.

Interval prediction of crude oil spot price volatility: An improved hybrid model integrating decomposition strategy, IESN and ARIMA

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By “divide and conquer”, excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction.

Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network

AbstractIn various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, many existing forecasting methods still only generate point forecasts. Although methods exist to generate probabilistic forecasts from these point forecasts, these are often limited to prediction intervals or must be trained together with a specific point forecast. Therefore, the present article proposes a novel approach for generating probabilistic forecasts from arbitrary point forecasts. In order to implement this approach, we apply a conditional Invertible Neural Network (cINN) to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary point forecast to generate probabilistic forecasts. We evaluate our approach by generating probabilistic forecasts from multiple point forecasts and comparing these forecasts to six probabilistic benchmarks on four data sets. We show that our approach generally outperforms all benchmarks with regard to CRPS and Winkler scores and generates probabilistic forecasts with the narrowest prediction intervals whilst remaining reasonably calibrated. Furthermore, our approach enables simple point forecasting methods to rank highly in the Global Energy Forecasting Competition 2014.

Assessing the Impact of Education on Life Expectancy in Rwanda

Purpose: Education's impact on life expectancy is a crucial area of study, particularly in nations undergoing rapid demographic and epidemiological transitions like Rwanda. This research examines the intricate relationship between education and life expectancy in Rwanda, considering the nation's advancements in education and healthcare. Despite progress, challenges persist in ensuring equitable access to quality education. Methodology: The study utilizes quantitative research methods, employing descriptive, correlation, and causal-comparative approaches, alongside econometric analyses like co-integration tests and vector error correction models. Data covering the period from 1965 to 2020 were collected from the World Bank Database. Findings: Findings indicate a significant effect of education on fertility rates, with higher education levels associated with lower fertility. Additionally, forecasts project an increase in life expectancy across educational groups, albeit less pronounced for the less educated. Gender disparities persist, with the rate of decrease in life expectancy differences between males and females slower than in previous decades. Increasing prediction intervals highlight growing uncertainty over time. Unique Contribution to Theory, Practice and Policy: The research contributes to understanding the complex interplay between education and life expectancy in Rwanda, offering insights for policy interventions to enhance population health outcomes and promote sustainable development. This study underscores the imperative for targeted strategies to mitigate educational barriers and advance inclusive development, addressing existing gaps in empirical evidence within the Rwandan context.

Semantic Segmentation for Change Detection in Satellite Imaging

Change detection is a common and actual problem in the field of remote sensing. The classical approaches using raw pixel information are very sensitive to noise. In this study we propose the usage of additional semantic information for change detection. We use the semantic segmentation methods like geospatial Segment Anything Model and encoder based U-Net to evaluate the predictions and tracing the semantic information as well as raw information in change detection. Later the multidimensional time series data is used via the Vector Autoregression model to predict the future changes in the landscape. The observations which fall out of the prediction interval are considered as the changes in the landscape. The proposed method is evaluated on the dataset of the random locations across the Baltic region. The research is accompanied by the data and reproducible code at Github repository.

Integrating Neutrosophic Numbers in Regression Analysis for Enhancing Predictive Modelling through Uncertainty Representation

This article explores the integration of neutrosophic statistics into regression analysis to bolster predictive modeling. Neutrosophic statistics, an extension of interval statistics, offer a robust framework for handling uncertainty and indeterminacy in datasets. By augmenting traditional interval predictions with measures of indeterminacy, neutrosophic numbers provide a nuanced representation of uncertainty, empowering decision-makers with a comprehensive view of potential outcomes. The methodology emphasizes the iterative refinement of predictive models to adapt to evolving data dynamics and escalating uncertainties. Future research directions include investigating the impact of different neutrosophic aggregation techniques on model performance, exploring synergies with other machine learning paradigms, and extending the applicability of neutrosophic statistics to diverse domains beyond regression analysis. By addressing these avenues, this study aims to advance the frontier of predictive modeling and facilitate more informed decision-making in complex and uncertain environments.

Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for evaluating wind farms' power quality and operation. Typically, the performance of prediction interval (PI) generating models; is restricted in terms of computational efficiency inheritably from the sequential data processing; whereas restrictions in forecast quality are derived from the PI generation techniques. This paper presents an efficient Gated Multi-Scale Convolutional Sequence Model (GMSCSM) to forecast wind speed PIs. GMSCSM while conserving the ‘recurrence’ of LSTMs also offers ‘parallel input’ advantage of CNNs for better computational efficiency which is highly desirable for short-term forecasting models. In addition, capturing features at various scales in the sequence, GMSCSM learns both local details and global context which is vital for multi-horizon forecasts. The model generates quality PIs utilizing an improved quality-driven loss which we proposed by invoking calibration assessment in its existing definition. Forecasts generated for eight different datasets obtained from two different wind farms show an improvement of 30 % and 6 % in the average coverage width criterion index while reducing the model training time to nearly half and one third of the respective values obtained from traditional and LSTM based models which showcases the proposed model's excellent prediction capability and efficiency.

A global time series of traffic volumes on extra-urban roads

Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.

APOE4 homozygozity represents a distinct genetic form of Alzheimer's disease.

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer's disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer's Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Change in clinical parameters after subgingival instrumentation for the treatment of periodontitis and timing of periodontal re-evaluation: A systematic review and meta-analysis.

To evaluate the changes in periodontal parameters (reduction in probing pocket depth [PPD], gain in clinical attachment level [CAL] and reduction in full-mouth bleeding on probing [BoP]) after subgingival instrumentation of periodontal pockets at different time points in systemically healthy patients suffering from periodontitis. Four databases were searched for RCTs that carried out subgingival instrumentation in periodontal pockets and evaluated PPD at a minimum of two consecutive time points other than baseline. The analysis was conducted for both all pocket depths and stratified for initially shallow (4-5 mm) and deep (≥6 mm) pockets and data were extracted for various time points, 1-2, 3-4 and 5-6 months. Weighted mean effects (WMEs) were calculated with 95% confidence interval (CI) and predictive intervals were calculated. Twenty-nine RCTs were identified, and all of them were included in the meta-analysis. The results showed that for both shallow and deep pockets there was a small though clinically meaningful change between 1- to 2-month and 3- to 4-month time points and between these and 5-6 months. In systemically healthy patients, the greater part of reduction in PPD and gain in CAL occurs within the first 1-2 months after subgingival instrumentation. However, additional benefits in terms of pocket depth reduction occur beyond these early time points.

No client left behind: A meta-analysis of premature termination from psychotherapy in U.S. service members and veterans.

Dropout has been identified as a significant problem among military populations seeking psychotherapy (Goetter et al., 2015; Hoge et al., 2014), yet an overall estimate of its exact prevalence and predictors does not exist. The aims of the current meta-analysis were to estimate outpatient psychotherapy dropout rates for this population and evaluate potential moderators of this event. In total, 283 articles-comprising data from 719,465 U.S. service members and veterans-met all inclusion criteria and were included in the meta-analysis. The average weighted dropout rate for all outpatient therapies was 25.6%, 95% CI [22.4%, 29.2%], and prediction interval [1.9%, 85.9%]. Furthermore, dropout was 27.0% for cognitive behavioral therapies (CBTs), 25.3% for trauma treatments, 27.6% for the Department of Veterans Affairs (VA), 28.9% for individual therapies, and 9.8% for intensive outpatient settings. Findings from metaregression analyses using mixed-effects models indicated that higher dropout was linked with the following after accounting for other moderators: younger age, CBTs, nonmanualized approaches, VA versus Department of Defense settings, individual versus group therapies, and weekly versus intensive outpatient formats. Dropout was not linked with other client, therapist, treatment, and research variables. Taken together, dropout estimates were obtained for a range of military populations and treatment characteristics, including theoretical orientation, presenting concern, setting, and therapy formats. These estimates may provide potential benchmarks for therapists, administrators, and policymakers serving military populations. Leveraging dropout prevention strategies with at-risk groups highlighted in this study may enhance mental health care outcomes for this high-need population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prevalence of perinatal post-traumatic stress disorder (PTSD) in low-income and middle-income countries: a systematic review and meta-analysis

ObjectivesTo systematically synthesise the evidence on prevalence of perinatal post-traumatic stress disorder (PTSD) in low-income and middle-income countries (LMICs).DesignSystematic review and meta-analysis.Data sourcesMEDLINE, Embase, PsycINFO, Scopus, Web of Science, Global...

Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk

This paper proposes tests for out-of-sample comparisons of interval forecasts based on parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage with respect to the set of conditioning variables from all models, for a given loss function. We propose a pairwise test to compare two models for a single predictive interval. The set-up is then extended to a comparison across multiple models and/or intervals. The limiting distribution varies depending on whether models are strictly non-nested or overlapping. In the latter case, degeneracy may occur. We establish the asymptotic validity of wild bootstrap based critical values across all cases. An empirical application to Growth-at-Risk (GaR) uncovers situations in which a richer set of financial indicators are found to outperform a commonly-used benchmark model when predicting downside risk to economic activity.

Using neural ordinary differential equations to predict complex ecological dynamics from population density data.

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data (Chen et al. 2018 Adv. Neural Inf. Process. Syst.). Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here, we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. We also find that untuned NODEs have a similar forecasting accuracy to untuned long-short term memory neural networks and both are outperformed in accuracy and precision by empirical dynamical modelling . However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy. We also discuss ways to improve the forecasting performance of NODEs. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.

Building consumption anomaly detection: A comparative study of two probabilistic approaches

This paper investigates the performance of two probabilistic approaches, Ensemble batch Prediction Intervals (EnbPI) a conformal prediction approach and XGBoost Location, Scale and Shape (XGBoostLSS), in predicting building energy consumption and in detecting systemic anomalies with proposed alarm matrix. The research questions focus on the effectiveness of these models in providing both point and probabilistic predictions and their utility in identifying collective anomalies. Both models showed good point and distribution prediction performance. For example, the observed point prediction had CV-RMSE in the range of 9 to 17%, outperforming recommendations from ASHRAE. Furthermore, a post-processing stage, the alarm matrix, effectively flags collective, repetitive anomalies, thus shifting focus from conventional point anomalies. The EnbPI-based method yields higher recall rates, with a trade-off of having more false alarms, while XGBoostLSS-based method excels in precision, minimizing overlooked alarms. Moreover, a robustness analysis was carried out to evaluate how these models performed when faced with training datasets containing anomalies. The robustness analysis revealed that the EnbPI-based method was more prone to overfitting, meaning its performance actually improved when the training data included some noise. On the other hand, the XGBoostLSS-based method was more stable, performing well with low levels of noise with performance drop when the noise level was high. While the findings contribute significantly to building energy consumption prediction and anomaly detection, future research could address performance in dynamic environments for the methodology and explore continual learning strategies.

Allometric exponents for scaling running economy in human samples: A systematic review and Meta-analysis

Ratio-scaled VO2 is the widely used method for quantifying running economy (RE). However, this method should be criticized due to its theoretical defect and curvilinear relationship indicated by the allometric scaling, although no consensus has been achieved on the generally accepted exponent b value of body weight. Therefore, this study aimed to provide a quantitative synthesis of the reported exponents used to scale VO2 to body weight. Six electronic databases were searched based on related terms. Inclusion criteria involved human cardiopulmonary testing data, derived exponents, and reported precision statistics. The random-effects model was applied to statistically analyze exponent b. Subgroup and meta-regression analyses were conducted to explore the potential factors contributing to variation in b values. The probability of the true exponent being below 1 in future studies was calculated. The estimated b values were all below 1 and aligned with the 3/4 power law, except for the 95% prediction interval of the estimated fat-free body weight exponent b. A publication bias and a slightly greater I2 and τ statistic were also observed in the fat-free body weight study cohort. The estimated probabilities of the true body weight exponent, full body weight exponent, and fat-free body weight exponent being lower than 1 were 93.8% (likely), 95.1% (very likely), and 94.5% (likely) respectively. ‘Sex difference’, ‘age category’, ‘sporting background’, and ‘testing modality’ were four potential but critical variables that impacted exponent b. Overall, allometric-scaled RE should be measured by full body weight with exponent b raised to 3/4.

A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties

The uncertainties of renewable energies and loads in integrated energy system (IES) result in unstable operation and performance degradation. To consider the impact of the uncertainties, the scheduling strategies of IES with the prediction of uncertainties are required. This paper designs a hybrid solar and gas turbine IES, in which the thermal and electrical storage units are integrated to address the uncertainties. A robust real-time energy scheduling strategy is proposed according to the real-time load and the predicted loads in the future time, in which the errors of multi-step interval prediction of renewable sources and loads are combined. The multi-step prediction method based on gated recurrent unit and time classification is constructed for the interval prediction of uncertain sources and loads by analyzing the probability statistics of prediction errors. In the robust optimization, the penalty factors of time sequences are proposed and considered to decrease the influences of future predicted information on current real-time scheduling, in which the future uncertainties in larger interval with current time has less influence. The optimal results in the hybrid IES demonstrate that the daily operation cost is 13.67% lower than the method that does not consider the prediction parameters. Compared to the traditional scheduling strategies, the proposed strategy declines the operation cost by 2.93%. The analysis of the confidence level of predictions ranging from 60% to 98% illustrates that the operation cost under the penalty factor of 0.94 is the lowest when the confidence level is from 94% to 96%.

Effects of SGLT2 inhibitors on right ventricular function in heart failure patients: Updated meta-analysis of the current literature.

There is some discrepancy in current studies concerning the effect of sodium-glucose cotransporter type 2 inhibitors (SGLT2i) on right ventricular (RV) functions in heart failure (HF) patients. Hence, this meta-analysis was focused on determining the impact of SGLT2i on RV functions in such individuals. Two independent investigators searched PubMed, Google Scholar, and the Cochrane Library for articles of interest. To analyze heterogeneity, Higgins' I2 as well as prediction intervals and Egger's test were used to assess heterogeneity. The Newcastle-Ottawa standard ratings approach was used to assess the quality of observational studies. The ROBINS-I risk of bias algorithm was used to assess bias risks of randomized studies. This meta-analysis evaluated 8 studies in total. Over the follow-up time frame, patients who used SGLT2i had substantially lower systolic pulmonary artery pressure and higher tricuspid annular plane systolic excursion values (mean difference [MD] = -5.23 [-7.81; -2.66] and, MD = 1.47 [1.01; 1.93]; P <0.01, respectively). There was no significant difference in RVS' values between follow-up and baseline (MD = 1.54 [-0.19; 3.26]; P = 0.08). However, as compared to the baseline period, fractional area contraction values were substantially larger at the end of the follow-up (MD = 5.52 [4.23; 6.82]; P <0.01). To the best of our knowledge, this is the first meta-analysis assessing the impact of SGLT2i on RV function in HF patients. Our findings suggest that SGLT2i may improve RV performance in HF patients.

날씨 변수를 고려한 전력 수요량 예측구간 시계열 분석 연구

Annual electricity demand is steadily increasing and the importance of forecasting the electricity demand is emerging since the dependence on domestic electricity is also increasing. The purpose of this study is analysis of the electricity demand considering seasonality and weather variables, for which we study time series modelling and forecasting analysis. ARIMA, SARIMA, ARIMAX, SARIMAX models are handled with optimally selected orders. Out-of-sample forecasting analysis is carried out to evaluate predicted values and their errors such as RMSE, MAE, MAPE and MSLE. The errors by four models are compared. As the optimal model, SARIMAX(2,1,1) (2,1,2,12) model with CDD, HDD and insolation has been chosen. For strict statistical analysis of prediction intervals, normal distribution, laplace distribution and bootstrap method are adopted to construct 80%, 95% prediction intervals. Coverage probability (CP), average length (AL) and mean interval score (MIS) of the prediction intervals by three approximations are evaluated. This study contributes with novelty in that it provides three approaches of prediction intervals along with their comparison. The results of this study are expected to be of practical help in the efficient operation of electricity supply system.